Semi-extractive multi-document summarization

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dc.contributor.authorGhiyafeh Davoodi, Fatemeh
dc.contributor.authorUniversity of Lethbridge. Faculty of Arts and Science
dc.contributor.supervisorChali, Yllias
dc.date.accessioned2015-10-02T20:21:39Z
dc.date.available2015-10-02T20:21:39Z
dc.date.issued2015
dc.degree.levelMastersen_US
dc.description.abstractIn this thesis, I design a Maximum Coverage problem with KnaPsack constraint (MCKP) based model for extractive multi-document summarization. The model integrates three measures to detect important sentences including Coverage, rewards sentences in regards to their representative level of the whole document, Relevance, focuses to select sentences that related to the given query, and Compression, rewards concise sentences. To generate a summary, I apply an efficient and scalable greedy algorithm. The algorithm has a near optimal solution when its scoring functions are monotone non-decreasing and submodular. I use DUC 2007 dataset to evaluate our proposed method. Investigating the results using ROUGE package shows improvement over two closely related works. The experimental results illustrates that integrating compression in the MCKP-based model, applying semantic similarity measures to detect Relevance measure and also defining all scoring functions as a monotone submodular function result in having a better performance in generating a summary.en_US
dc.embargoNoen_US
dc.identifier.urihttps://hdl.handle.net/10133/3759
dc.language.isoen_CAen_US
dc.proquest.subject0984en_US
dc.proquestyesYesen_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.subjectgreedy algorithmen_US
dc.subjectknapsacken_US
dc.subjectmaximum coverageen_US
dc.subjectmulti-documenten_US
dc.subjectsummarizationen_US
dc.titleSemi-extractive multi-document summarizationen_US
dc.typeThesisen_US
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