Journal ArticleDOI
GA, MR, FFNN, PNN and GMM based models for automatic text summarization
Mohamed Abdel Fattah,Fuji Ren +1 more
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TLDR
This work proposes an approach to address the problem of improving content selection in automatic text summarization by using some statistical tools, which takes into account several features, including sentence position, positive keyword, negative keyword, sentence centrality, sentence resemblance to the title, sentenceclusion of name entity, sentence inclusion of numerical data, sentence relative length and aggregated similarity.About:
This article is published in Computer Speech & Language.The article was published on 2009-01-01. It has received 235 citations till now. The article focuses on the topics: Sentence & Automatic summarization.read more
Citations
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Recent automatic text summarization techniques: a survey
Mahak Gambhir,Vishal Gupta +1 more
TL;DR: A comprehensive survey of recent text summarization extractive approaches developed in the last decade is presented and the discussion of useful future directions that can help researchers to identify areas where further research is needed are discussed.
Journal ArticleDOI
Assessing sentence scoring techniques for extractive text summarization
Rafael Ferreira,Luciano Cabral,Rafael Dueire Lins,Gabriel de França Pereira e Silva,Fred Freitas,George D. C. Cavalcanti,Rinaldo Lima,Steven J. Simske,Luciano Favaro +8 more
TL;DR: A quantitative and qualitative assessment of 15 algorithms for sentence scoring available in the literature are described and directions to improve the sentence extraction results obtained are suggested.
Posted Content
Natural Language Processing: State of The Art, Current Trends and Challenges
TL;DR: The paper distinguishes four phases by discussing different levels of NLP and components of Natural Language Generation (NLG) followed by presenting the history and evolution ofNLP, state of the art presenting the various applications of N LP and current trends and challenges.
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MCMR: Maximum coverage and minimum redundant text summarization model
TL;DR: An unsupervised text summarization model which generates a summary by extracting salient sentences in given document(s) by using an integer linear programming problem, which is quite general and can also be used for single- and multi-document summarization.
References
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Journal ArticleDOI
Probabilistic neural networks
TL;DR: A probabilistic neural network that can compute nonlinear decision boundaries which approach the Bayes optimal is formed, and a fourlayer neural network of the type proposed can map any input pattern to any number of classifications.
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The automatic creation of literature abstracts
TL;DR: In the exploratory research described, the complete text of an article in machine-readable form is scanned by an IBM 704 data-processing machine and analyzed in accordance with a standard program.
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LexRank: graph-based lexical centrality as salience in text summarization
Gunes Erkan,Dragomir R. Radev +1 more
TL;DR: LexRank as discussed by the authors is a stochastic graph-based method for computing relative importance of textual units for Natural Language Processing (NLP), which is based on the concept of eigenvector centrality.
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The use of MMR, diversity-based reranking for reordering documents and producing summaries
Jaime Carbinell,Jade Goldstein +1 more
TL;DR: A method for combining query-relevance with information-novelty in the context of text retrieval and summarization and preliminary results indicate some benefits for MMR diversity ranking in document retrieval and in single document summarization.