Toward abstractive multi-document summarization using submodular function-based framework, sentence compression and merging

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dc.contributor.authorTanvee, Moin Mahmud
dc.contributor.authorUniversity of Lethbridge. Faculty of Arts and Science
dc.contributor.supervisorChali, Yllias
dc.date.accessioned2017-05-03T19:08:40Z
dc.date.available2017-05-03T19:08:40Z
dc.date.issued2016
dc.degree.levelMastersen_US
dc.description.abstractAutomatic multi-document summarization is a process of generating a summary that contains the most important information from multiple documents. In this thesis, we design an automatic multi-document summarization system using different abstraction-based methods and submodularity. Our proposed model considers summarization as a budgeted submodular function maximization problem. The model integrates three important measures of a summary - namely importance, coverage, and non-redundancy, and we design a submodular function for each of them. In addition, we integrate sentence compression and sentence merging. When evaluated on the DUC 2004 data set, our generic summarizer has outperformed the state-of-the-art summarization systems in terms of ROUGE-1 recall and f1-measure. For query-focused summarization, we used the DUC 2007 data set where our system achieves statistically similar results to several well-established methods in terms of the ROUGE-2 measure.en_US
dc.embargoNoen_US
dc.identifier.urihttps://hdl.handle.net/10133/4841
dc.language.isoen_USen_US
dc.proquestyesNoen_US
dc.publisherLethbridge, Alta : University of Lethbridge, Dept. 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.subjectabstraction-baseden_US
dc.subjectsubmodular functionen_US
dc.subjectgeneric-focused summarizationen_US
dc.subjectquery-focused summarizationen_US
dc.subjectgreedy algorithmen_US
dc.subjectNatural language processing (Computer science) -- Researchen_US
dc.subjectQuerying (Computer science)en_US
dc.subjectDatabase searchingen_US
dc.subjectParsing (Computer grammar)en_US
dc.subjectInformation retrievalen_US
dc.subjectQuestion-answering systems -- Researchen_US
dc.subjectComputer science -- Mathematicsen_US
dc.titleToward abstractive multi-document summarization using submodular function-based framework, sentence compression and mergingen_US
dc.typeThesisen_US
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