164 Sentences With "summarization" | Random Sentence Generator

Part of the problem with summarization is that something is always left out.

Text generation is key part of language translation, chatbots, question-answering, and summarization.

The researchers explain that automatic text summarization works in two ways: extraction or abstraction.

The app was rooted in Summly, a summarization app created by teenage developer Nick D'Aloisio.

You can leverage Transformers for text classification, information extraction, summarization, text generation and conversational artificial intelligence.

And they're penciling in Spring 2018 for the full AI-powered meeting summarization feature to launch, he adds.

Automatic summarization would be a particularly useful technology for Salesforce, which produces a variety of customer-service focused products.

What's no spoiler whatsoever is the summarization that, in terms of how Abzû plays, it's essentially a sequel to Journey.

Stephen Colbert's Late Show came roaring back to life with the host's giddy summarization of the latest in Trump scandals.

It also started to demonstrate some talent for reading comprehension, summarization, and translation with no explicit training in those tasks.

More effective text summarization tools would unlock serious value for Salesforce users — if the research community can finish working out the kinks.

Sidharth says the summarization technology used is a lot like those that summarize news articles, except it's been designed specifically for chats.

Automatic summarization is a big remaining challenge that, if solved, would accelerate discovery by focusing researchers on the most pressing problems in their fields.

Summarization is the name of the process, as it is when you or I do it, and other bots and services do it, as well.

The OpenAI researchers found that GPT-2 performed very well when it was given tasks that it wasn't necessarily designed for, like translation and summarization.

It was a smart and neat choice to use the cover to relay story information instead of a summarization of the contents of the comic.

In some ways, Macaw is similar to Nuzzel, another Twitter summarization app that provides a list of top links that your network is sharing and discussing.

Belyaev says the plan is to add integrations with other communications tools — such as Google Hangouts, Zoom and Slack — in order to "capture more information to improve our summarization engine".

The Vatican announced on Thursday that the pontiff revised the Catechism of the Catholic Church, the church's written summarization of its teachings, to categorically oppose capital punishment in all circumstances.

Firstly, in order for a puny human brain to interpret large and complex data sets, the data sets must first be made "smaller" via aggregation, summarization, description and presentation, which kind of misses the point.

In the future, better summarization can help create a connected web of academic research that can make it much easier to understand the state of the art in a field, and encourage scientists to re-test colleagues' results.

Genevieve: I'm fascinated that both of you picked out that shot of Paige in the parking lot as a setup for some big action that never arrived, when I thought it was clearly included for thematic/summarization purposes, not narrative ones.

Truly, we have succeeded in developing a first prototype which also shows that there is still a long way to go: the extractive summarization of large text corpora is still imperfect, and paraphrased texts, syntax and phrase association still seem clunky at times.

" GTP-2, for those who don't make neural-net generated recipes in their spare time, was developed by OpenAI and is a "a large-scale unsupervised language model which generates coherent paragraphs of text, achieves state-of-the-art performance on many language modeling benchmarks, and performs rudimentary reading comprehension, machine translation, question answering, and summarization—all without task-specific training.

Y.) on Tuesday said Attorney General William BarrWilliam Pelham BarrThe Hill's Morning Report - Trump searches for backstops amid recession worries Mueller report fades from political conversation Barr removes prisons chief after Epstein death MORE "must answer" for reports that special counsel Robert MuellerRobert (Bob) Swan MuellerMueller report fades from political conversation Trump calls for probe of Obama book deal Democrats express private disappointment with Mueller testimony MORE objected to Barr's summarization of the conclusions in the investigation into Russia's election interference.

There are broadly two types of extractive summarization tasks depending on what the summarization program focuses on. The first is generic summarization, which focuses on obtaining a generic summary or abstract of the collection (whether documents, or sets of images, or videos, news stories etc.). The second is query relevant summarization, sometimes called query-based summarization, which summarizes objects specific to a query. Summarization systems are able to create both query relevant text summaries and generic machine-generated summaries depending on what the user needs.

Towards content-oriented patent document processing: Intelligent patent analysis and summarization. World Patent Information, 40, 30-42. focused on patent summarization.

66 information retrieval, and automatic summarization. Klavans, Judith L. (2004) “Text Summarization”. Berkshire Encyclopedia of Human-Computer Interaction. William S. Bainbridge, editor.

"A Class of Submodular Functions for Document Summarization", The 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL-HLT), 2011 shows that many existing systems for automatic summarization are instances of submodular functions. This was a breakthrough result establishing submodular functions as the right models for summarization problems. Submodular Functions have also been used for other summarization tasks. Tschiatschek et al.

Automatic summarization is the process of shortening a set of data computationally, to create a subset (a summary) that represents the most important or relevant information within the original content. In addition to text, images and videos can also be summarized. Text summarization finds the most informative sentences in a document; image summarization finds the most representative images within an image collection ; video summarization extracts the most important frames from the video content.

Approaches aimed at higher summarization quality rely on combined software and human effort. In Machine Aided Human Summarization, extractive techniques highlight candidate passages for inclusion (to which the human adds or removes text). In Human Aided Machine Summarization, a human post-processes software output, in the same way that one edits the output of automatic translation by Google Translate.

There are two general approaches to automatic summarization: extraction and abstraction.

An intrinsic evaluation tests the summarization system in and of itself while an extrinsic evaluation tests the summarization based on how it affects the completion of some other task. Intrinsic evaluations have assessed mainly the coherence and informativeness of summaries. Extrinsic evaluations, on the other hand, have tested the impact of summarization on tasks like relevance assessment, reading comprehension, etc.

Domain independent summarization techniques generally apply sets of general features which can be used to identify information-rich text segments. Recent research focus has drifted to domain-specific summarization techniques that utilize the available knowledge specific to the domain of text. For example, automatic summarization research on medical text generally attempts to utilize the various sources of codified medical knowledge and ontologies.

It also includes a timeline and a summarization of the intended audience.

An example of a summarization problem is document summarization, which attempts to automatically produce an abstract from a given document. Sometimes one might be interested in generating a summary from a single source document, while others can use multiple source documents (for example, a cluster of articles on the same topic). This problem is called multi-document summarization. A related application is summarizing news articles.

Outlines are used for composition, summarization, and as a development and storage medium.

Like keyphrase extraction, document summarization aims to identify the essence of a text. The only real difference is that now we are dealing with larger text units—whole sentences instead of words and phrases. Before getting into the details of some summarization methods, we will mention how summarization systems are typically evaluated. The most common way is using the so-called ROUGE (Recall-Oriented Understudy for Gisting Evaluation) measure.

Submodular functions have achieved state-of-the-art for almost all summarization problems. For example, work by Lin and Bilmes, 2012Hui Lin, Jeff Bilmes. "Learning mixtures of submodular shells with application to document summarization", UAI, 2012 shows that submodular functions achieve the best results to date on DUC-04, DUC-05, DUC-06 and DUC-07 systems for document summarization. Similarly, work by Lin and Bilmes, 2011,Hui Lin, Jeff Bilmes.

This paper presents a method for recognizing omissible case elements considering application to such summarization.

The first publication in the area dates back to 1958 (Lun), starting with a statistical technique. Research increased significantly in 2015. Term frequency–inverse document frequency had been used by 2016. Pattern-based summarization was the most powerful option for multi-document summarization found by 2016.

Proceedings of the International Joint Conference on, vol. 3. IEEE, 2003, pp. 1915–1920. and automatic summarization.

Multi-document summarization is an automatic procedure aimed at extraction of information from multiple texts written about the same topic. Resulting summary report allows individual users, such as professional information consumers, to quickly familiarize themselves with information contained in a large cluster of documents. In such a way, multi- document summarization systems are complementing the news aggregators performing the next step down the road of coping with information overload. Multi-document summarization may also be done in response to a question.

A summary is formed by combining the top ranking sentences, using a threshold or length cutoff to limit the size of the summary. It is worth noting that TextRank was applied to summarization exactly as described here, while LexRank was used as part of a larger summarization system (MEAD) that combines the LexRank score (stationary probability) with other features like sentence position and length using a linear combination with either user-specified or automatically tuned weights. In this case, some training documents might be needed, though the TextRank results show the additional features are not absolutely necessary. Another important distinction is that TextRank was used for single document summarization, while LexRank has been applied to multi-document summarization.

"Paraphrasing" is even more difficult to apply to image and video, which is why most summarization systems are extractive.

"Improving Diversity in Ranking using Absorbing Random Walks." HLT-NAACL. 2007. In addition to explicitly promoting diversity during the ranking process, GRASSHOPPER incorporates a prior ranking (based on sentence position in the case of summarization). The state of the art results for multi-document summarization, however, are obtained using mixtures of submodular functions.

During the DUC 2001 and 2002 evaluation workshops, TNO developed a sentence extraction system for multi-document summarization in the news domain. The system was based on a hybrid system using a naive Bayes classifier and statistical language models for modeling salience. Although the system exhibited good results, the researchers wanted to explore the effectiveness of a maximum entropy (ME) classifier for the meeting summarization task, as ME is known to be robust against feature dependencies. Maximum entropy has also been applied successfully for summarization in the broadcast news domain.

Automatic summarization consists in finding a set of images from a larger image collection that represents such collection. Different methods based on clustering have been proposed to select these image prototypes (summary). The summarization process addresses the problem of selecting a representative set of images of a search query or in some cases, the overview of an image collection.

Imagine a system, which automatically pulls together news articles on a given topic (from the web), and concisely represents the latest news as a summary. Image collection summarization is another application example of automatic summarization. It consists in selecting a representative set of images from a larger set of images.Jorge E. Camargo and Fabio A. González.

This is also called the core-set. These algorithms model notions like diversity, coverage, information and representativeness of the summary. Query based summarization techniques, additionally model for relevance of the summary with the query. Some techniques and algorithms which naturally model summarization problems are TextRank and PageRank, Submodular set function, Determinantal point process, maximal marginal relevance (MMR) etc.

Many natural language processing applications, like question answering, information extraction, summarization, multi-document summarization, and evaluation of machine translation systems, need to recognize that a particular target meaning can be inferred from different text variants. Typically entailment is used as part of a larger system, for example in a prediction system to filter out trivial or obvious predictions.

Seq2seq is a family of machine learning approaches used for language processing. Applications include language translation, image captioning, conversational models and text summarization.

LexRankGüneş Erkan and Dragomir R. Radev: LexRank: Graph-based Lexical Centrality as Salience in Text Summarization is an algorithm essentially identical to TextRank, and both use this approach for document summarization. The two methods were developed by different groups at the same time, and LexRank simply focused on summarization, but could just as easily be used for keyphrase extraction or any other NLP ranking task. In both LexRank and TextRank, a graph is constructed by creating a vertex for each sentence in the document. The edges between sentences are based on some form of semantic similarity or content overlap.

Summarization here is intended to encompass any means of aggregating or compressing the query results into a more human- consumable form. Faceted search, described above, is one such form of summarization. Another is clustering, which analyzes a set of documents by grouping similar or co-occurring documents or terms. Clustering allows the results to be partitioned into groups of related documents.

ROUGE, or Recall-Oriented Understudy for Gisting Evaluation,Lin, Chin-Yew. 2004. ROUGE: a Package for Automatic Evaluation of Summaries. In Proceedings of the Workshop on Text Summarization Branches Out (WAS 2004), Barcelona, Spain, July 25 - 26, 2004. is a set of metrics and a software package used for evaluating automatic summarization and machine translation software in natural language processing.

This is similar to densely connected Web pages getting ranked highly by PageRank. This approach has also been used in document summarization, considered below.

3, no.6, pp.36-42. # Abdi A., Idris N., Alguliyev R.M., Aliguliyev R.M., An automated summarization assessment algorithm for identifying summarizing strategies. PLoS ONE 2016, vol.

In the following year it was surpassed by latent semantic analysis (LSA) combined with non-negative matrix factorization (NMF). Although they did not replace other approaches and are often combined with them, by 2019 machine learning methods dominated the extractive summarization of single documents, which was considered to be nearing maturity. By 2020 the field was still very active and research is shifting towards abstractive summation and real-time summarization.

40, no.5, pp.1675–1689. (SCI) (USA) # Alguliev R.M., Aliguliyev R.M., Isazade N.R. CDDS: constraint-driven document summarization models // Expert Systems with Applications, 2013, vol.40, no.

Goals of the Translingual Information Detection, Extraction and Summarization (TIDES) project Translingual Information Detection, Extraction and Summarization (TIDES) developing advanced language processing technology to enable English speakers to find and interpret critical information in multiple languages without requiring knowledge of those languages. Outside groups (such as universities, corporations, etc.) were invited to participate in the annual information retrieval, topic detection and tracking, automatic content extraction, and machine translation evaluations run by NIST.

7, iss.4, pp.38-66. (USA) # Alguliyev R.M., Aliguliyev R.M., Isazade N.R., Abdi A., Idris N. A Model for Text Summarization. International Journal of Intelligent Information Technologies 2017, vol.

Rocchio, J. (1971). Relevance feedback in information retrieval. In: Salton, G (ed), The SMART Retrieval System. Summarization and analytics help users digest the results that come back from the query.

The unsupervised approach to summarization is also quite similar in spirit to unsupervised keyphrase extraction and gets around the issue of costly training data. Some unsupervised summarization approaches are based on finding a "centroid" sentence, which is the mean word vector of all the sentences in the document. Then the sentences can be ranked with regard to their similarity to this centroid sentence. A more principled way to estimate sentence importance is using random walks and eigenvector centrality.

It explains coherence by postulating a hierarchical, connected structure of texts. In 2000, Daniel Marcu, also of ISI, demonstrated that practical discourse parsing and text summarization also could be achieved using RST.

Knowledge-Based Systems. 2017, vol.135, 1 November, pp.135-146. (Netherlands) (Scopus) # Alguliyev R.M., Aliguliyev R.M., Abdi A., Idris N. Query-based multi-documents summarization using linguistic knowledge and content word expansion.

Mineo and Wordsplayed called their song, "Dunk Contest", a summarization of their time in Atlanta.Madden, Sidney. “Andy Mineo and Wordsplayed Discuss Their New Mixtape 'Magic & Bird'.” XXL, Townsquare Media, August 3, 2017, www.xxlmag.

1, no.8, pp.503-507. (USA) # Alguliev R.M., Aliguliyev R.M. Summarization of text-based documents with a determination of latent topical sections and information-rich sentences // Automatic Control and Computer Sciences, 2007, vol.

1, no.2, pp.33-38. (USA) # Alguliyev R.M., Aliguliyev R.M., Mehdiyev C.A. An optimization model and DPSO-EDA for document summarization // International Journal of Information Technology and Computer Science, 2011, vol.3, no.

Multi-document extractive summarization faces a problem of potential redundancy. Ideally, we would like to extract sentences that are both "central" (i.e., contain the main ideas) and "diverse" (i.e., they differ from one another).

International Journal of Knowledge Management Studies. 2015, vol.6(1), pp.31-62.(UK) Scopus # Abdi A., Idris N., Alguliyev R.M., Aliguliyev R.M. Query-based multi-documents summarization using linguistic knowledge and content word expansion.

Intra-textual methods assess the output of a specific summarization system, and the inter-textual ones focus on contrastive analysis of outputs of several summarization systems. Human judgement often has wide variance on what is considered a "good" summary, which means that making the evaluation process automatic is particularly difficult. Manual evaluation can be used, but this is both time and labor- intensive as it requires humans to read not only the summaries but also the source documents. Other issues are those concerning coherence and coverage.

2, pp.458-465.(USA) Web of Science # Alguliev R.M., Aliguliyev R.M., Isazade N.R. Formulation of document summarization as a 0–1 nonlinear programming problem // Computers & Industrial Engineering, 2013, vol.64, no.1, pp.94-102.

Video summarization is a related domain, where the system automatically creates a trailer of a long video. This also has applications in consumer or personal videos, where one might want to skip the boring or repetitive actions. Similarly, in surveillance videos, one would want to extract important and suspicious activity, while ignoring all the boring and redundant frames captured. At a very high level, summarization algorithms try to find subsets of objects (like set of sentences, or a set of images), which cover information of the entire set.

The most common way to evaluate the informativeness of automatic summaries is to compare them with human-made model summaries. Evaluation techniques fall into intrinsic and extrinsic,Mani, I. Summarization evaluation: an overview inter-textual and intra-textual.

Dominating sets are of practical interest in several areas. In wireless networking, dominating sets are used to find efficient routes within ad-hoc mobile networks. They have also been used in document summarization, and in designing secure systems for electrical grids.

For uses with visual impairment, it has a built-in screen reader with summarization. Websites can also access analytics about their user's disabilities. It is available for free for websites with less than 10,000 pageviews per month, and has a subscription model for larger websites.

Lexipedia is an online visual semantic network with dictionary and thesaurus reference functionality built on Vantage Learning's Multilingual ConceptNet.G. Adriaens. Language Engineering Applications: INFORMATION RETRIEVAL AND SUMMARIZATION. ccl.kuleuven.be (2003-02-20) Lexipedia presents words with their semantic relationships displayed in an animated visual word web.

The major downside of applying sentence-extraction techniques to the task of summarization is the loss of coherence in the resulting summary. Nevertheless, sentence extraction summaries can give valuable clues to the main points of a document and are frequently sufficiently intelligible to human readers.

A year later, in 2016, Schwartz published the second part, "Maarten Maartens Rediscovered - His Best Short Stories," a summarization with quotations of thirty-two short stories selected from Maartens' four published volumes of short stories, as well as The Black Box Murder, Maartens' first self-published detective.

The methodology of MMIR can be organized in three groups: # Methods for the summarization of media content (feature extraction). The result of feature extraction is a description. # Methods for the filtering of media descriptions (for example, elimination of redundancy) # Methods for the categorization of media descriptions into classes.

Nemhauser, George L., Laurence A. Wolsey, and Marshall L. Fisher. "An analysis of approximations for maximizing submodular set functions—I." Mathematical Programming 14.1 (1978): 265-294. Moreover, the greedy algorithm is extremely simple to implement and can scale to large datasets, which is very important for summarization problems.

As with point-to-point services, point-to- multipoint services are provisioned using a unique HVID per service. Encapsulation is not required and frames can be forwarded using HVID only. Summarization of HVIDs reduces the size of forwarding tables and creates scalability. Millions of point-to-multipoint services can be provided.

Recent advances in the field of Video Synopsis have resulted in methods that focus in collecting key- points(or frames) from the long uncut video and presenting them as a chain of key events that summarize the video. As mentioned in,Muhammad Ajmal, Muhammad Husnain Ashraf, Muhammad Shakir, Yasir Abbas, Faiz Ali Shah, Video Summarization: Techniques and Classification this is only one of the many methods employed in modern literature to perform this task. Recently, these event-driven methods have focused on correlating objects in frames, but in a more semantically related way that has been called a story-driven method of summarizing video. These methods have been shown to work well for egocentricZheng Lu, Kristen Grauman Story-driven summarization for egocentric video.

The set cover function attempts to find a subset of objects which cover a given set of concepts. For example, in document summarization, one would like the summary to cover all important and relevant concepts in the document. This is an instance of set cover. Similarly, the facility location problem is a special case of submodular functions.

TIDES is an ambitious technology development effort, funded by DARPA. It stands for Translingual Information Detection, Extraction and Summarization. It is focused on the automated processing and understanding of a variety of human language data. The primary goal is to make it possible for English speakers to find and interpret needed information quickly and effectively regardless of language or medium.

Example of setting up EIGRP on a Cisco IOS router for a private network. The 0.0.15.255 wildcard in this example indicates a subnetwork with a maximum of 4094 hosts—it is the bitwise complement of the subnet mask 255.255.240.0. The no auto-summary command prevents automatic route summarization on classful boundaries, which would otherwise result in routing loops in discontiguous networks.

The idea of a submodular set function has recently emerged as a powerful modeling tool for various summarization problems. Submodular functions naturally model notions of coverage, information, representation and diversity. Moreover, several important combinatorial optimization problems occur as special instances of submodular optimization. For example, the set cover problem is a special case of submodular optimization, since the set cover function is submodular.

Sepp Hochreiter developed "Factor Analysis for Robust Microarray Summarization" (FARMS). FARMS has been designed for preprocessing and summarizing high-density oligonucleotide DNA microarrays at probe level to analyze RNA gene expression. FARMS is based on a factor analysis model which is optimized in a Bayesian framework by maximizing the posterior probability. On Affymetrix spiked-in and other benchmark data, FARMS outperformed all other methods.

Central to urban planning and architectural design > of a city is the creation of an individual and unique visage for that city. > The architecture must embody both the progressive traditions as well as the > past experiences of the people. #For urban planning, as for architectural > design, there shall be no abstract scheme. Crucial are only the > summarization of essential architectural factors and the demands of daily > life.

For example, a search for "java" might return clusters for Java (programming language), Java (island), or Java (coffee). Visual representation of data is also considered a key aspect of HCIR. The representation of summarization or analytics may be displayed as tables, charts, or summaries of aggregated data. Other kinds of information visualization that allow users access to summary views of search results include tag clouds and treemapping.

A Multi-class Kernel Alignment Method for Image Collection Summarization. In Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications (CIARP '09), Eduardo Bayro-Corrochano and Jan-Olof Eklundh (Eds.). Springer-Verlag, Berlin, Heidelberg, 545-552. A summary in this context is useful to show the most representative images of results in an image collection exploration system.

Supervised text summarization is very much like supervised keyphrase extraction. Basically, if you have a collection of documents and human- generated summaries for them, you can learn features of sentences that make them good candidates for inclusion in the summary. Features might include the position in the document (i.e., the first few sentences are probably important), the number of words in the sentence, etc.

Silber and McCoy also investigates text summarization, but their approach for constructing the lexical chains runs in linear time. Some authors use WordNet to improve the search and evaluation of lexical chains. Budanitsky and Kirst compare several measurements of semantic distance and relatedness using lexical chains in conjunction with WordNet. Their study concludes that the similarity measure of Jiang and Conrath presents the best overall result.

The idea behind compression techniques is to maintain only a synopsis of the data, but not all (raw) data points of the data stream. The algorithms range from selecting random data points called sampling to summarization using histograms, wavelets or sketching. One simple example of a compression is the continuous calculation of an average. Instead of memorizing each data point, the synopsis only holds the sum and the number of items.

Rhetorical structure theory (RST) was originally developed by William Mann and Sandra Thompson of the University of Southern California's Information Sciences Institute (ISI) and defined in a seminal paper in 1988. This theory was developed as part of studies of computer based text generation. Natural language researchers later began using RST in text summarization and other applications. RST addresses text organization by means of relations that hold between parts of text.

A retelling of Superman's origin story in 1948 first delved into detail about Jor-El. However, his formal and more familiar Silver Age aspects were firmly established starting in the late 1950s. Over the course of the next several decades, there was a definitive summarization in the miniseries World of Krypton in 1979 (not to be confused with the similarly-named post-Crisis on Infinite Earths late-1980s comic miniseries).

Lincoln Barnett's style is populist rather than mathematical. Totally absent are the calculations and traditional proofs of geology and the other natural sciences. He does repeat or summarize some statistics derived from those sciences of the times, without much reference to the sources. His work is a selective summarization of some of the major scientific theories about "the world we live in," greatly enhanced by prize-winning art and photography.

The first published work by a neural network was published in 2018, 1 the Road, marketed as a novel, contains sixty million words. Both these systems are basically elaborate but non-sensical (semantics-free) language models. The first machine-generated science book was published in 2019 (Beta Writer, Lithium-Ion Batteries, Springer, Cham). Unlike Racter and 1 the Road, this is grounded on factual knowledge and based on text summarization.

Description is the fiction-writing mode for transmitting a mental image of the particulars of a story. Together with dialogue, narration, exposition, and summarization, description is one of the most widely recognized of the fiction-writing modes. Description is more than the amassing of details, it is bringing a scene to life by carefully choosing and arranging words and phrases to produce the desired effect.Polking, 1990, p. 106.

Sentence extraction is a technique used for automatic summarization of a text. In this shallow approach, statistical heuristics are used to identify the most salient sentences of a text. Sentence extraction is a low-cost approach compared to more knowledge-intensive deeper approaches which require additional knowledge bases such as ontologies or linguistic knowledge. In short "sentence extraction" works as a filter which allows only important sentences to pass.

Hierarchical VLAN is a proposed extension to VLAN which, like PBB and PBT, turns cost-efficient Ethernet into a flexible, carrier-grade transport technology. Unlike other technologies, HVLAN uses the mature VLAN functionality to support all connectivity schemes: point-to-point, point-to-multipoint, and multipoint-to-multipoint. It uses a hierarchical VLAN allocation technique to achieve this. The technique allows summarization to reduce the number of forwarding table entries within the carrier network switches.

Scenes that are important to the story should be dramatized with showing, but sometimes what happens between scenes can be told so the story can make progress. According to Orson Scott Card and others, "showing" is so terribly time-consuming that it is to be used only for dramatic scenes. The objective is to find the right balance of telling versus showing, summarization versus action. Factors like rhythm, pace, and tone come into play.

Flowchart showing how the MAS5 algorithm by Agilent works. Factor Analysis for Robust Microarray Summarization (FARMS) is a model-based technique for summarizing array data at perfect match probe level. It is based on a factor analysis model for which a Bayesian maximum a posteriori method optimizes the model parameters under the assumption of Gaussian measurement noise. According to the Affycomp benchmark FARMS outperformed all other summarizations methods with respect to sensitivity and specificity.

Dragomir R. Radev is a Yale University professor of computer science working on natural language processing and information retrieval. He previously served as a University of Michigan computer science professor and Columbia University computer science adjunct professor. Radev serves as Member of the Advisory Board of Lawyaw. He is currently working in the fields of open domain question answering, multi-document summarization, and the application of NLP in Bioinformatics, Social Network Analysis and Political Science.

He joined GTE Laboratories, where he worked on intelligent interfaces relating to databases. In 1989, he proposed a new project at GTE called "Knowledge Discovery in Databases". The project created advanced prototypes, including KEFIR (Key Findings Reporter), a system for analysis and summarization of key changes in large databases, which was a forerunner of systems like Google Analytics Intelligence. A KEFIR prototype was applied to GTE health care data and received GTE's highest technical award.

Moreover, submodular functions can be efficiently combined together, and the resulting function is still submodular. Hence, one could combine one submodular function which models diversity, another one which models coverage and use human supervision to learn a right model of a submodular function for the problem. While submodular functions are fitting problems for summarization, they also admit very efficient algorithms for optimization. For example, a simple greedy algorithm admits a constant factor guarantee.

Web users copy on websites different things for different reasons, including words and phrases to look up elsewhere, key sentences for use in citations and text summaries, and programming code fragments for use in software development. Tracking and recording copy operations of users and using that data as implicit user feedback on the website content can be beneficial in a wide range of applications and uses, including in automatic text summarization, and in text simplification.

The cover page of Manushyalaya Chandrika published by Chowkhamba Krishnadas Academy, Varanasi. Manushyalaya Chandrika is a sixteenth century CE treatise in Sanskrit dealing with domestic architecture. The work is authored by Thirumangalath Neelakanthan Musath and is a summarization of the basic principles of domestic architecture then widely followed in that region of India now known as Kerala State. The popularity of the text as a basic reference of traditional Kerala architecture has continued even to modern times.

Together with dialogue, narration, exposition, and summarization, description is one of the most widely recognized of the fiction-writing modes. As stated in Writing from A to Z, edited by Kirk Polking, description is more than the amassing of details; it is bringing something to life by carefully choosing and arranging words and phrases to produce the desired effect. The most appropriate and effective techniques for presenting description are a matter of ongoing discussion among writers and writing coaches.

He received his Ph.D. in Computer Science from Yale University in 1979. At the time of his appointment, Carbonell was the youngest chaired professor in the School of Computer Science at CMU. He was considered creative, insightful, and highly productive as a researcher. His research spanned several areas of computer science, mostly in artificial intelligence, including: machine learning, data and text mining, natural language processing, very-large-scale knowledge bases, translingual information retrieval and automated summarization.

Notably, Slaise's health and athletic knowledge fueled the development of sports and nutrition company "Austerus FIT" in 2011, which she later sold in 2016 citing a desire to become deeply embedded in the enhancement of the local community. Slaise retired from the United States Air Force as a Major in 2018. Her officership career appointment summarization includes APU/PACU/CASF/Emergency Department Clinical Nurse, Medical Group & Squadron Executive Officer, POTUS Flight Medicine Clinic Flight Commander and Flight Nurse.

Some writing stylistic differences are present in Kenyan novels. Often, writers use exposition, summarization, and description, rather than the principle show, don't tell of fiction writing. For example, Monica Genya's Links of a Chain is written in third person omniscient, and Genya's writing focuses on telling the audience about detailed actions and elaborate backstories. Reviewer for World Literature Today James Gibbs writes on the novel's perception of reality: “The author is more interested in action than character or community.

Emmanuel Drake del Castillo (28 December 1855 - 14 May 1904) was a French botanist. He was born at Paris and studied with Louis Édouard Bureau (1830–1918) at the Muséum national d'histoire naturelle (National Museum of Natural History). Between 1886 and 1892, he published Illustrationes Florae Insulae Maris Pacifici ("Illustrations of the flora of the islands of the Pacific Ocean") a summarization of his work on the flora of French Polynesia. He also studied the flora of Madagascar.

Translingual Information Detection, Extraction and Summarization (TIDES) develops advanced language processing technology to enable English speakers to find and interpret critical information in multiple languages without requiring knowledge of those languages. Outside groups (such as universities, corporations, etc.) were invited to participate in the annual information retrieval, topic detection and tracking, automatic content extraction, and machine translation evaluations run by NIST. Cornell University, Columbia University, and the University of California, Berkeley were given grants to work on TIDES.

Summarization requires the reader to perform the task of discriminating between important and less- important information in the text. It must then be organized into a coherent whole (Palincsar & Brown, 1984). Summarizing is the process of identifying the important information, themes, and ideas within a text and integrating these into a clear and concise statement that communicates the essential meaning of the text. Summarizing may be based on a single paragraph, a section of text, or an entire passage.

The Smolin–Susskind debate refers to the series of intense postings in 2004 between Lee Smolin and Susskind, concerning Smolin’s argument that the "anthropic principle cannot yield any falsifiable predictions, and therefore cannot be a part of science." It began on July 26, 2004, with Smolin's publication of "Scientific alternatives to the anthropic principle." Smolin e-mailed Susskind asking for a comment. Having not had the chance to read the paper, Susskind requested a summarization of his arguments.

O'Kroley argued that the act of summarizing a non-defamatory article into a defamatory summary created new content and turned Google into an information content provider and thus losing its protected status. However, courts have found the immunities provided under the statute as "quite robust." The magistrate judge reviewing the case found that Google's automated editing and summarization process falls under the role of a publisher and thus Google has immunity from liability.Report and recommendation, O'Kroley v.

Seminal papers which laid the foundations for many techniques used today have been published by Hans Peter Luhn in 1958 and H. P Edmundson in 1969. Luhn proposed to assign more weight to sentences at the beginning of the document or a paragraph. Edmundson stressed the importance of title-words for summarization and was the first to employ stop-lists in order to filter uninformative words of low semantic content (e.g. most grammatical words such as "of", "the", "a").

Each step builds upon the other, which follows in order. The first step of observation involves an examination of words, structure, structural relationships and literary forms. After observations are formed, then the second step of interpretation involves asking interpretative questions, formulating answers to those questions, integration and summarization of the passage. After the meaning is derived through interpretation, the third step of application involves determining both the theoretical and practical significance of the text and appropriately applying this significance to today's modern context.

Similarly, Akerman's visual language resists easy categorization and summarization: she creates narrative through filmic syntax instead of plot development. Akerman was influenced by European art cinema as well as structuralist film. Structuralist film used formalist experimentation to propose a reciprocal relationship between image and viewer. Akerman cites Michael Snow as a structuralist inspiration, especially his film Wavelength, which is composed of a single shot of a photograph of a sea on a loft wall, with the camera slowly zooming in.

One of the metrics used in NIST's annual Document Understanding Conferences, in which research groups submit their systems for both summarization and translation tasks, is the ROUGE metric (Recall-Oriented Understudy for Gisting Evaluation ). It essentially calculates n-gram overlaps between automatically generated summaries and previously-written human summaries. A high level of overlap should indicate a high level of shared concepts between the two summaries. Note that overlap metrics like this are unable to provide any feedback on a summary's coherence.

Simcoe's elementary schools are quite rudimentary by Southwestern Ontario standards. Grocery stores and restaurants provide an average variety of service by Southwestern Ontario standards while the cafes and daycares are a bit lacklustre. Simcoe is not known for its vibrant nightlife, even though the community had several bars and taverns back in the 1980s and the 1990s.Basic summarization of Simcoe, Ontario at RE/MAX Norfolk Street tends to be the dividing road between the "haves" and the "have nots" in this community.

In 1997, a Japanese counterpart of TREC was launched, called National Institute of Informatics Test Collection for IR Systems (NTCIR). NTCIR conducts a series of evaluation workshops for research in information retrieval, question answering, text summarization, etc. A European series of workshops called the Cross Language Evaluation Forum (CLEF) was started in 2001 to aid research in multilingual information access. In 2002, the Initiative for the Evaluation of XML Retrieval (INEX) was established for the evaluation of content-oriented XML retrieval systems.

Marilyn A. Walker is an American computer scientist. She is professor of computer science and head of the Natural Language and Dialogue Systems Lab at the University of California, Santa Cruz (UCSC). Her research includes work on computational models of dialogue interaction and conversational agents, analysis of affect, sarcasm and other social phenomena in social media dialogue, acquiring causal knowledge from text, conversational summarization, interactive story and narrative generation, and statistical methods for training the dialogue manager and the language generation engine for dialogue systems.

350x350px Drill Down/Up allows the user to navigate among levels of data ranging from the most summarized (up) to the most detailed (down). The picture shows a drill-down operation: The analyst moves from the summary category "Outdoor-Schutzausrüstung" to see the sales figures for the individual products. Roll-up: A roll-up involves summarizing the data along a dimension. The summarization rule might be an aggregate function, such as computing totals along a hierarchy or applying a set of formulas such as "profit = sales - expenses".

The Transformer is a deep learning model introduced in 2017, used primarily in the field of natural language processing (NLP). Like recurrent neural networks (RNNs), Transformers are designed to handle sequential data, such as natural language, for tasks such as translation and text summarization. However, unlike RNNs, Transformers do not require that the sequential data be processed in order. For example, if the input data is a natural language sentence, the Transformer does not need to process the beginning of it before the end.

WordNet has been used for a number of purposes in information systems, including word-sense disambiguation, information retrieval, automatic text classification, automatic text summarization, machine translation and even automatic crossword puzzle generation. A common use of WordNet is to determine the similarity between words. Various algorithms have been proposed, including measuring the distance among words and synsets in WordNet's graph structure, such as by counting the number of edges among synsets. The intuition is that the closer two words or synsets are, the closer their meaning.

It is difficult to make any general statements about human (or animal) concept learning without already assuming a particular psychological theory of concept learning. Although the classical views of concepts and concept learning in philosophy speak of a process of abstraction, data compression, simplification, and summarization, currently popular psychological theories of concept learning diverge on all these basic points. The history of psychology has seen the rise and fall of many theories about concept learning. Classical conditioning (as defined by Pavlov) created the earliest experimental technique.

A supernetwork, or supernet, is an Internet Protocol (IP) network that is formed by combination of multiple networks (or subnets) into a larger network. The new routing prefix for the combined network represents the constituent networks in a single routing table entry. The process of forming a supernet is called supernetting, prefix aggregation, route aggregation, or route summarization. Supernetting within the Internet serves as a strategy to avoid topological fragmentation of the IP address space by using a hierarchical allocation system that delegates control of segments of address space to regional network service providers.

However, he did not anticipate the more complex and matured system of unit proportions embodied in the extensive written work by scholar-official Li Jie (1065–1110), the Treatise on Architectural Methods (營造法式; Yingzao Fashi) of 1103.Ruitenbeek (1996), 26–27. Klaas Ruitenbeek states that the version of the Timberwork Manual quoted by Shen is most likely Shen's summarization of Yu's work or a corrupted passage of the original by Yu Hao, as Shen writes: "According to some, the work was written by Yu Hao."Ruitenbeek (1996), 26.

The text indicates that it was translated and compiled in Turfan. The Compendium (Chinese: Moni guang-fu jiao-fa yi-lüe, lit. "outline of the teachings and rules of Mani, Buddha of Light") begins with an account of Mani's birth that is directly based on the life of the Buddha and then provides a summarization of Manichaean doctrines. The text opens with a paragraph that explains how the text was ordered by the Tang dynasty on July 16, 731, and in a later passage mentions how Mani was a reincarnation of Lao-tzu.

However, the man who had the document copied over a century later most likely had a different reason. When theorizing about the purposes of the copyist, it seems to be all-too-common to forget about the reverse side of the papyrus. This concerns, as near as we can tell, the "sending of commodities by Ni-ki.. through the agency of Ne-pz-K-r-t for unspecified payment." It could be that this is a summarization of an attempt to perform a mission similar to that of Wenamun in this later time.

He was affiliated with the Language Technologies Institute, Computer Science Department, Machine Learning Department, and Computational Biology Department at Carnegie Mellon. His interests spanned several areas of artificial intelligence, language technologies and machine learning. In particular, his research focused on areas such as text mining (extraction, categorization, novelty detection) and in new theoretical frameworks such as a unified utility-based theory bridging information retrieval, summarization, free-text question-answering and related tasks. He also worked on machine translation, both high-accuracy knowledge-based MT and machine learning for corpus-based MT (such as generalized example-based MT).

PVA casualties were estimated at 1,500 dead and 4,000 wounded.McWilliams, p.435 According to抗美援朝战争卫生工作总结卫生勤务 (Summarization of Medical Works in the War to Aid Korea and Resist America), 6,800 soldiers of 67th division were involved in 1953 summer battles for five days, among them 533 were killed and 1,242 were wounded. Less than three weeks after the Battle of Pork Chop Hill, the Korean Armistice Agreement was signed by the UN, PVA and North Korean Korean People's Army, ending the hostilities.

Most ATM networks supporting SPVPs, SPVCs, and SVCs use the Private Network Node Interface or the Private Network-to-Network Interface (PNNI) protocol to share topology information between switches and select a route through a network. PNNI is a link-state routing protocol like OSPF and IS-IS. PNNI also includes a very powerful route summarization mechanism to allow construction of very large networks, as well as a call admission control (CAC) algorithm which determines the availability of sufficient bandwidth on a proposed route through a network in order to satisfy the service requirements of a VC or VP.

"Versatile question answering systems: seeing in synthesis", International Journal of Intelligent Information Database Systems, 5(2), 119-142, 2011. Multi-document summarization creates information reports that are both concise and comprehensive. With different opinions being put together and outlined, every topic is described from multiple perspectives within a single document. While the goal of a brief summary is to simplify information search and cut the time by pointing to the most relevant source documents, comprehensive multi-document summary should itself contain the required information, hence limiting the need for accessing original files to cases when refinement is required.

Yaroslavtsev completed his PhD in computer science in three years in 2013 at Pennsylvania State University, advised by Sofya Raskhodnikova. His dissertation was titled Efficient Combinatorial Techniques in Sparsification, Summarization and Testing of Large Datasets. After an ICERM institute postdoctoral fellowship at Brown University, he joined the University of Pennsylvania in the first cohort of fellows at the Warren Center for Network and Data Science, founded by Michael Kearns. In 2016, Yaroslavtsev joined the faculty at Indiana University in the Department of Computer Science and founded the Center for Algorithms and Machine Learning (CAML) at Indiana University.

In the past, these Loading Time videos would feature behind the scenes footage for the creation of a specific sketch. After the launch of its Patreon in 2014, Loading Time videos shifted focus from specific sketches or types of content to a summarization of the work the team does on a monthly basis. Iron Stomach Challenge was a cooking show in which the crew would blend together random ingredients (usually suggested by their viewers), and attempt to eat or drink the usually-putrid concoction. Viewer suggestions were governed by several rules: it had to be safe to eat (i.e.

Point-to-point services are provisioned using a unique HVID per service. Planning HVIDs wisely enables summarization (as shown at the leftmost edge device) and reduces the number of forwarding entries to a strict minimum; the network now scales to support millions of point-to-point services with minimum packet overhead (it can be noted that no encapsulation was used, frames were forwarded using HVID only). A further example (see diagram) shows HVLAN operation in the case of point-to- multipoint services (e.g. IPTV). The diagram shows all forwarding table entries needed to transport the 2 multipoint services (red and blue) from a server (left) to 3 clients (right).

The tasks in this area have many potential applications, such as information extraction, question answering, document summarization, machine translation, construction of thesauri and semantic networks, language modeling, paraphrasing, and recognizing textual entailment. In each of these potential applications, the contribution of the types of semantic analysis constitutes the most outstanding research issue. For example, in the word sense induction and disambiguation task, there are three separate phases: #In the training phase, evaluation task participants were asked to use a training dataset to induce the sense inventories for a set of polysemous words. The training dataset consisting of a set of polysemous nouns/verbs and the sentence instances that they occurred in.

Berkshire Press, Great Barrington, Massachusetts. Her technologies have been applied in fields ranging from medical informatics, Judith Klavans and Smaranda Muresan. Evaluation of the DEFINDER System for Fully Automatic Glossary Construction. Proceedings of the American Medical Informatics Association Symposium (AMIA 2001) Kathleen R. Mckeown and Shih-fu Chang and James Cimino and Steven K. Feiner and Carol Friedman and Luis Gravano and Vasileios Hatzivassiloglou and Steven Johnson and Desmond A. Jordan and Desmond A and Judith L. Klavans and Andre Kushniruk and Vimla Patel and Simone Teufel}, PERSIVAL, a System for Personalized Search and Summarization over Multimedia Healthcare Information, In Proceedings of the First ACM+IEEE JCDL, 2001.

The main difficulty in supervised extractive summarization is that the known summaries must be manually created by extracting sentences so the sentences in an original training document can be labeled as "in summary" or "not in summary". This is not typically how people create summaries, so simply using journal abstracts or existing summaries is usually not sufficient. The sentences in these summaries do not necessarily match up with sentences in the original text, so it would be difficult to assign labels to examples for training. Note, however, that these natural summaries can still be used for evaluation purposes, since ROUGE-1 only cares about unigrams.

Foundations and Trends in Machine Learning, December 2012. A new method for multi-lingual multi-document summarization that avoids redundancy works by simplifying and generating ideograms that represent the meaning of each sentence in each document and then evaluates similarity "qualitatively" by comparing the shape and position of said ideograms has recently been developed. This tool does not use word frequency, does not need training or preprocessing of any kind and works by generating ideograms that represent the meaning of each sentence and then summarizes using two user-supplied parameters: equivalence (when are two sentences to be considered equivalent) and relevance (how long is the desired summary).

These include HBase, a distributed column-oriented database which provides random access read/write capabilities; Hive which is a data warehouse system built on top of Hadoop that provides SQL-like query capabilities for data summarization, ad hoc queries, and analysis of large datasets; and Pig – a high-level data-flow programming language and execution framework for data-intensive computing. Pig was developed at Yahoo! to provide a specific language notation for data analysis applications and to improve programmer productivity and reduce development cycles when using the Hadoop MapReduce environment. Pig programs are automatically translated into sequences of MapReduce programs if needed in the execution environment.

Some of Carbonell's major scientific accomplishments included the creation of MMR (maximal marginal relevance) technology for text summarization and informational novelty detection in search engines, invention of transformational analogy, a generalized method for case-based reasoning (CBR) to re-use, modify and compose past successful plans for increasingly complex problems and Knowledge- based interlingual machine translation. He was instrumental in setting up the Computational Biolinguistics Program, a joint venture between Carnegie Mellon and the University of Pittsburgh, which combines Language Technologies and Machine Learning to model and predict genomic, proteomic and glycomic 3D structures. Carbonell was particularly well known in machine learning. He organized the first four machine learning conferences, starting with CMU in 1981.

Bailey argued that the book was "an innovative, energetically argued, and important book, as varied and rich as the period and genres it opens up for us so fruitfully and eloquently." She argued that sometimes it seemed like "modernity" was used to mean "variety". Bailey believed that some of the summarization present in the book did not adequately cover its depth due to the coverage of works unfamiliar at the time to the audience and the book's scope itself. She stated that sometimes there was too much repetition, believing that Wang might have been unsure about the comprehension levels of his readers, and that there were errors in romanizations and other mistakes in typing.

Brendan John Frey FRSC (born 29 August 1968) is a Canadian-born entrepreneur, engineer and scientist. He is Founder and CEO of Deep Genomics, Cofounder of the Vector Institute for Artificial Intelligence and Professor of Engineering and Medicine at the University of Toronto. Frey is a pioneer in the development of machine learning and artificial intelligence methods, their use in accurately determining the consequences of genetic mutations, and in designing medications that can slow, stop or reverse the progression of disease. As far back as 1995, Frey co-invented one of the first deep learning methods, called the wake-sleep algorithm, the affinity propagation algorithm for clustering and data summarization, and the factor graph notation for probability models.

Since 2002, Shang Yang has attempted to collage and print mechanically-reproduced images on his already-mature “the Great Landscape”. Shang Yang: the Dong Qichang Project explores the idea that the aggressive intervention of contemporary culture has fragmented and flattened the solid traditional Chinese logic of self-sufficiency, harmony and unity. This exhibition marks Shang Yang's first solo show within more than five decades of art production; it promises to deliver a comprehensive display of his impressive skill and conceptual development presenting in his latest artworks. His self-imposed mission to “contribute to modernism” in the pursuit of art has had wide practical significance to the development of Chinese contemporary art. His “Dong Qichang Project” is a summarization of his “Big Scenery Series”.

In this paper, he also introduced TF-IDF, or term-frequency-inverse- document frequency, a model in which the score of a term in a document is the ratio of the number of terms in that document divided by the frequency of the number of documents in which that term occurs. (The concept of inverse document frequency, a measure of specificity, had been introduced in 1972 by Karen Sparck-Jones.) Later in life, he became interested in automatic text summarization and analysis, as well as automatic hypertext generation. He published over 150 research articles and 5 books during his life. Salton was editor-in-chief of the Communications of the ACM and the Journal of the ACM, and chaired Special Interest Group on Information Retrieval (SIGIR).

The quality the codec can achieve is heavily based on the compression format the codec uses. A codec is not a format, and there may be multiple codecs that implement the same compression specification for example, MPEG-1 codecs typically do not achieve quality/size ratio comparable to codecs that implement the more modern H.264 specification. But quality/size ratio of output produced by different implementations of the same specification can also vary. Each compression specification defines various mechanisms by which raw video (in essence, a sequence of full-resolution uncompressed digital images) can be reduced in size, from simple bit compression (like Lempel-Ziv-Welch) to psycho-visual and motion summarization, and how the output is stored as a bit stream.

The Chinese City Creativity Index is based on the theoretical methods related to Michael Porter's diamond Model, Systems Theory, etc. A Chinese City Creativity Index model is established, which consists of 4 modules including factor driving force, demand pull force, relevant support force and industrial influence force, 9 secondary indexes and 18 tertiary indexes. The model takes into account not only the promoting effect of various resources such as talents, funds, technologies and culture, but also the pulling function of cultural needs and consumption potentials as well as the supporting role of relevant industries such as communication and network. Moreover, all the indexes adopt the form of both absolute value and relative value, and the index summarization method uses a multiplicative model rather than an additive model.

During that period, Šitler also chaired the Working Group for the Summarization of Property Wrongs within the Mixed Commission Dealing with the Mitigation of Certain Property Wrongs suffered by Holocaust Victims (presided over by the Deputy Prime Minister Pavel Rychetský). He was later appointed Ambassador Extraordinary and Plenipotentiary of the Czech Republic to the Kingdom of Thailand, the Kingdom of Cambodia, the Lao PDR and the Union of Myanmar/Burma. Šitler' s mission began in May 2001 and ended in December 2006. He returned to the headquarters of the Ministry of Foreign Affairs in Prague, as Director of Diplomatic Protocol (December 2006 – November 2007), followed by a position as Director of the Asia and Pacific Department (April 2008 – October 2010).

Her model describes the process of learning to understand reading as nonlinear, wherein "language, literacy, cognitive, social, and other environmental factors" develop in a mutual feedback process over time. The model incorporates several previous models of reading comprehension and child development with the addition of child-instruction interaction effects, putting together numerous child characteristics and home- and classroom- characteristics that interact as a system over time. She developed and applied this model during her time working on the Florida State University team of the IES Reading for Understanding Research Initiative with Principal Investigator Christopher J. Lonigan. Recent Work In 2017, Connor received a four-year IES grant to develop electronic-books intended to provide adaptive support to improve reading comprehension, including strategies such as word learning, question generation, and summarization.

Online discussion platforms may be designed and improved to streamline discussions for efficiency, usefulness and quality. For instance voting, targeted notifications, user levels, gamification, subscriptions, bots, discussion requirements, structurization, layout, sorting, linking, feedback-mechanisms, reputation-features, demand- signaling features, requesting-features, visual highlighting, separation, curation, tools for real-time collaboration, tools for mobilization of humans and resources, standardization, data-processing, segmentation, summarization, moderation, time-intervals, categorization/tagging, rules and indexing can be leveraged in synergy to improve the platform. In 2013 Sarah Perez claimed that the best platform for online discussion doesn't yet exist, noting that comment sections could be more useful if they showed "which comments or shares have resonated and why" and which "understands who deserves to be heard". Online platforms don't intrinsically guarantee informed citizen input.

He extended support vector machines to handle kernels that are not positive definite with the "Potential Support Vector Machine" (PSVM) model, and applied this model to feature selection, especially to gene selection for microarray data. Also in biotechnology, he developed "Factor Analysis for Robust Microarray Summarization" (FARMS). Sepp Hochreiter introduced modern Hopfield newtworks with continuous states and applied them to the task of immune repertoire classification. In addition to his research contributions, Sepp Hochreiter is broadly active within his field: he launched the Bioinformatics Working Group at the Austrian Computer Society; he is founding board member of different bioinformatics start-up companies; he was program chair of the conference Bioinformatics Research and Development; he is a conference chair of the conference Critical Assessment of Massive Data Analysis (CAMDA); and he is editor, program committee member, and reviewer for international journals and conferences.

Therefore, objective opinion analysis systems are suggested as a solution to this in the form of automated opinion mining and summarization systems. Marketers using this type of intelligence to make inferences about consumer opinion should be wary of what is called opinion spam, where fake opinions or reviews are posted in the web in order to influence potential consumers for or against a product or service. Search engines are a common type of intelligence that seeks to learn what the user is interested in to present appropriate information. PageRank and HITS are examples of algorithms that search for information via hyperlinks; Google uses PageRank to control its search engine. Hyperlink based intelligence can be used to seek out web communities, which is described as ‘ a cluster of densely linked pages representing a group of people with a common interest’.

" He also praised the Control Center design, calling it "a great upgrade", though also highlighting the inability to easily switch Wi-Fi networks. Snell noted that the App Store's design had been unchanged for years, but received a full redesign in iOS 11, and wrote that Apple's commitment to editorial pages was "impressive", making the App Store "a richer, more fun experience." Regarding the introduction of augmented reality, he stated that most apps using it were "bad", though some also "mind-blowingly good," adding that the "huge potential" depended on how third-party apps were using it. Snell also praised improvements to the iPad experience, including multitasking and drag-and-drop across apps, the latter of which he stated "actually surpasses my expectations" due to ease of use. His review summarization states that iOS 11 is "Apple’s most ambitious and impressive upgrade in years.

They were available in flush- mount, single, and double projector versions, in either red or gray. These horns were mainly used by IBM, and later SimplexGrinnell up until the 1960s. In 1958, Benjamin was bought out by Thomas Industries, Inc. The company's factory in Illinois was closed in 1963, and was replaced with a new light fixture plant in Sparta, TN. Biography of Reuben Berkley Benjamin and background history of Benjamin Electric Manufacturing Company / Benjamin Electric, Ltd. London Citation and summarization from: • The Electrical World, June 2, 1923 • Article from The Alumnus of Iowa State, by Arch. R. Crawford, 1931 • In Memoriam of R. B. Benjamin, ’92; article from Vol. XXIX, January 1934 • Obituary - Reuben B. Benjamin: In Memoriam, January 1934 • Her Majesty Queen Elizabeth II, “EIIR The Coronation Booklet” – published by Holophane Ltd. November 9, 1953 Reuben Berkley Benjamin, born May 20, 1869, in Fulton, New York, to Timothy R. and Harriet E. Benjamin, and died December 26, 1933.

67, no.12, pp.1089-1095. (USA) # Alguliyev R.M., Abdullayev R.S. Analyses of surplus traffic load in urban infrastructure // Transport Problems, 2008, part 2, vol.3, no.4, pp.13-17. (Poland) # Алгулиев Р.М., Алыгулиев Р.М., Гусейнова А.А. Повышение эффективности корпоративных сетей с применением CDN-технологии // Информационные технологии, 2008, №7, с.2-9. (Москва) # Алгулиев Р.М., Абдуллаев Р.С. Анализ способов сокращения транспортных нагрузок на городскую инфраструктуру // Информационные технологии, 2008, №5, с.59-62. (Москва) # Алгулиев Р.М., Оруджов Г.Г., Сабзиев Э.Н., Панахов Н.А., Расулова Н.В., Алиева А.А. Разработка алгоритмов обнаружения и корректировки некоторых характерных ошибок векторной карты рельефа местности // Информационные технологии, 2008, №4, с.19-23. (Москва) # Алгулиев Р.М., Агаев Б.С., Фаталиев Т.Х., Алиев Т.С. Распределенная обработка аудиоинформации в корпоративных сетях на базе IP-телефонии // Телекоммуникации, 2008, №4, с.16-20. (Москва) # Alguliyev R.M., Aliguliyev R.M. Evolutionary algorithm for extractive text summarization // Intelligent Information Management, 2009, vol.1, no.2, pp.128-138.

Making decisions in many domains (such as natural language processing and computer vision problems) often involves assigning values to sets of interdependent variables where the expressive dependency structure can influence, or even dictate, what assignments are possible. These settings are applicable not only to Structured Learning problems such as semantic role labeling, but also for cases that require making use of multiple pre-learned components, such as summarization, textual entailment and question answering. In all these cases, it is natural to formulate the decision problem as a constrained optimization problem, with an objective function that is composed of learned models, subject to domain- or problem-specific constraints. Constrained conditional models form a learning and inference framework that augments the learning of conditional (probabilistic or discriminative) models with declarative constraints (written, for example, using a first-order representation) as a way to support decisions in an expressive output space while maintaining modularity and tractability of training and inference.

GO is the fifth studio album from the band Girugamesh, which was released on January 26, 2011 in Japan and on February 4 in Europe. Two editions of the album were released: a Regular Version CD and a Limited Version 2CD+DVD which includes the music videos for "COLOR [PV]" and "イノチノキ (Inochi no Ki) [PV]", a documentary, G-TRAVEL 2010 SUMMER, and a summarization of “Ura-Girugamesh” in 2010. GO was released to various countries in Europe and to the United States during the Here we go!! world tour starting on March 5, 2011 at The Tochka in Moscow, Russia and ending on April 27, 2011 at the JAXX Night Club in Washington DC. Girugamesh has confirmed that the tour will be continuing in Japan starting on June 4, 2011 at the Hiroshima Namiki Junction in Hiroshima, Japan and ending on June 26, 2011 at the Zepp Tokyo in Tokyo, Japan.

Even if Verizon had not caused the routing table to exceed 512k entries in the short spike, it would have happened soon anyway through natural growth. Route summarization is often used to improve aggregation of the BGP global routing table, thereby reducing the necessary table size in routers of an AS. Consider AS1 has been allocated the big address space of , this would be counted as one route in the table, but due to customer requirement or traffic engineering purposes, AS1 wants to announce smaller, more specific routes of , , and . The prefix does not have any hosts so AS1 does not announce a specific route . This all counts as AS1 announcing four routes. AS2 will see the four routes from AS1 (, , , and ) and it is up to the routing policy of AS2 to decide whether or not to take a copy of the four routes or, as overlaps all the other specific routes, to just store the summary, .

The Language Technologies Institute (LTI), founded and directed by Carbonell, achieved top honors in multiple areas. These areas include machine translation, search engines (including founding of Lycos by Michael Mauldin, one of Carbonell’s PhD students), speech synthesis, and education. LTI remains the original, largest and best-known institute for language technologies, with over $12M in annual funding and 200 researchers (faculty, staff, PhD students, MS students, visiting scholars etc.). Carbonell made major technical contributions in several fields, including (1) Creation of MMR (maximal marginal relevance) technology for text summarization and informational novelty detection in search engines,(2) Proactive machine learning for multi- source cost-sensitive active learning, (3) Linked conditional random fields for predicting tertiary and quaternary protein folds, (4) Symmetric optimal phrasal alignment method for trainable example-based and statistical machine translation, (5) Series- anomaly modeling for financial fraud detection and syndromic surveillance, (6) Knowledge-based interlingual machine translation, (7) Robust case-frame parsing, (8) Seeded version-space learning and (9) Invention of transformational and derivational analogy, generalized methods for case-based reasoning (CBR) to re-use, modify and compose past successful plans for increasingly complex problems.

Around 6 million Polish citizens perished during World War II: about one fifth of the pre-war population. Most were civilian victims of the war crimes and crimes against humanity during the occupation by Nazi Germany and the Soviet Union. Statistics for Polish World War II casualties are divergent and contradictory. This article provides a summarization of these estimates of Poland's human losses in the war and their causes. The official Polish government report on war damages prepared in 1947 put Poland's war dead at 6,028,000; 3.0 million ethnic Poles and 3.0 million Jews not including losses of Polish citizens from the Ukrainian and Belarusian ethnic groups. This figure was disputed when the communist system collapsed by the Polish historian Czesław Łuczak who put total losses at 6.0 million; 3.0 million Jews, 2.0 million ethnic Poles, and 1.0 million Polish citizens from the other ethnic groups not included in the 1947 report on war damages.Materski and Szarota page 16 In 2009 the Polish government-affiliated Institute of National Remembrance (IPN) published the study "Polska 1939–1945. Straty osobowe i ofiary represji pod dwiema okupacjami" (Poland 1939-1945. Human Losses and Victims of Repression Under the Two Occupations) that estimated Poland's war dead at between 5.6 and 5.8 million Poles and Jews, including 150,000 during the Soviet occupation.