Methods of sentence extraction, abstraction and ordering for automatic text summarization

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dc.contributor.authorNayeem, Mir Tafseer
dc.contributor.authorUniversity of Lethbridge. Faculty of Arts and Science
dc.contributor.supervisorChali, Yllias
dc.date.accessioned2017-12-09T23:35:19Z
dc.date.available2017-12-09T23:35:19Z
dc.date.issued2017
dc.degree.levelMastersen_US
dc.description.abstractIn this thesis, we have developed several techniques for tackling both the extractive and abstractive text summarization tasks. We implement a rank based extractive sentence selection algorithm. For ensuring a pure sentence abstraction, we propose several novel sentence abstraction techniques which jointly perform sentence compression, fusion, and paraphrasing at the sentence level. We also model abstractive compression generation as a sequence-to-sequence (seq2seq) problem using an encoder-decoder framework. Furthermore, we applied our sentence abstraction techniques to the multi-document abstractive text summarization. We also propose a greedy sentence ordering algorithm to maintain the summary coherence for increasing the readability. We introduce an optimal solution to the summary length limit problem. Our experiments demonstrate that the methods bring significant improvements over the state-of-the-art methods. At the end of this thesis, we also introduced a new concept called "Reader Aware Summary" which can generate summaries for some critical readers (e.g. Non-Native Reader).en_US
dc.description.sponsorshipNatural Sciences and Engineering Research Council (NSERC) of Canada and the University of Lethbridgeen_US
dc.embargoNoen_US
dc.identifier.urihttps://hdl.handle.net/10133/4993
dc.language.isoen_USen_US
dc.proquest.subject0723en_US
dc.proquest.subject0800en_US
dc.proquest.subject0984en_US
dc.proquestyesYesen_US
dc.publisherLethbridge, Alta. : Universtiy of Lethbridge, Department of Mathematics and Computer Scienceen_US
dc.publisher.departmentDepartment of Mathematics and Computer Scienceen_US
dc.publisher.facultyArts and Scienceen_US
dc.relation.ispartofseriesThesis (University of Lethbridge. Faculty of Arts and Science)en_US
dc.subjectautomatic text summarizationen_US
dc.subjectmulti-document text summarizationen_US
dc.subjectneural paraphrastic compressionen_US
dc.subjectsentence abstractionen_US
dc.subjectsequence-to-sequenceen_US
dc.titleMethods of sentence extraction, abstraction and ordering for automatic text summarizationen_US
dc.typeThesisen_US
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