Query-focused abstractive summarization using sequence-to-sequence and transformer models

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dc.contributor.authorPolash, Md Mainul Hasan
dc.contributor.authorUniversity of Lethbridge. Faculty of Arts and Science
dc.contributor.supervisorChali, Yllias
dc.date.accessioned2020-01-17T17:11:24Z
dc.date.available2020-01-17T17:11:24Z
dc.date.issued2019
dc.degree.levelMastersen_US
dc.description.abstractQuery Focused Summarization (QFS) summarizes a long document with respect to a given input query. Creating a query-focused abstractive summary by using a neural network model is a difficult task which is yet to be fully solved. In our thesis, we propose two neural network models for the query-focused abstractive summarization task. We propose a model based on the sequence-to-sequence architecture with a pointer-generator mechanism. Furthermore, we also use the transformer architecture to design a model for the abstractive summarization. Afterward, we train both our models with the Debatepedia dataset so that the model can learn to summarize a long document with respect to a query. We evaluate the output of our models against the human-created reference summary. Our transformer model outperforms our sequence-to-sequence model in all ROUGE scores.en_US
dc.identifier.urihttps://hdl.handle.net/10133/5665
dc.language.isoen_USen_US
dc.proquest.subjectComputer science [0984]en_US
dc.proquest.subjectComputer engineering [0464]en_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.subjectNatural language processingen_US
dc.subjectNeural computersen_US
dc.subjectSoftware engineeringen_US
dc.subjectSystems engineeringen_US
dc.subjectText data miningen_US
dc.titleQuery-focused abstractive summarization using sequence-to-sequence and transformer modelsen_US
dc.typeThesisen_US
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