Query-Focused Abstractive Summarization using Neural Networks

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dc.contributor.authorAryal, Chudamani
dc.contributor.authorUniversity of Lethbridge. Faculty of Arts and Science
dc.contributor.supervisorChali, Yllias
dc.date.accessioned2019-06-19T14:45:09Z
dc.date.available2019-06-19T14:45:09Z
dc.date.issued2019
dc.degree.levelMastersen_US
dc.description.abstractQuery-focused abstractive document summarization (QFADS) is a process of shortening a document into a summary while keeping the context of query in mind. We implemented a model consisting of a novel selective mechanism for QFADS. A selective mechanism was used for improving the representation of a long input (passage) sequence. We conducted experiments on the Debatepedia dataset, a recently developed dataset for query-focused abstractive summarization task, which showed that our model outperforms the state-of-the-art model in all ROUGE scores. Also, we proposed three models all of which consist of a coarse-to-fine approach and a novel selective mechanism for query-focused abstractive multi document summarization (QFAMDS). The coarse-to-fine approach was used to reduce the length of the passage input from multiple documents. We conducted experiments on the MS MARCO dataset, a recently developed large scale dataset by Microsoft for reading comprehension, and have reported our scores using various evaluation metrics.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/10133/5400
dc.language.isoen_USen_US
dc.proquest.subjectComputer science [0984]en_US
dc.proquest.subjectArtificial intelligence [0800]en_US
dc.proquest.subjectComputer engineering [0464]en_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.relation.ispartofseriesThesis (University of Lethbridge. Faculty of Arts and Science)
dc.subjectNeural networksen_US
dc.subjectQuery-focused abstractive document summarizationen_US
dc.subjectQuery-focused abstractive multi document summarizationen_US
dc.titleQuery-Focused Abstractive Summarization using Neural Networksen_US
dc.typeThesisen_US
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