Query-based summarization using reinforcement learning and transformer model

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dc.contributor.authorMahmud, Asif
dc.contributor.authorUniversity of Lethbridge. Faculty of Arts and Science
dc.contributor.supervisorChali, Yllias
dc.date.accessioned2020-01-17T16:56:36Z
dc.date.available2020-01-17T16:56:36Z
dc.date.issued2020
dc.degree.levelMastersen_US
dc.description.abstractQuery-based summarization problem is an interesting problem in the text summarization field. On the other hand, the reinforcement learning technique is popular for robotics and becoming accessible for the text summarization problem in the last couple of years (Narayan et al., 2018). The lack of significant works using reinforcement learning to solve the query-based summarization problem inspired us to use this technique. While doing so, We also introduce a different approach for sentence ranking and clustering to avoid redundancy in summaries. We propose an unsupervised extractive summarization method, which provides state-of-the-art results on some metrics. We develop two abstractive multi-document summarization models using the reinforcement learning technique and the transformer model (Vaswani et al., 2017). We consider the importance of information coverage and diversity under a fixed sentence limit for our summarization models. We have done several experiments for our proposed models, which bring significant results across different evaluation metrics.en_US
dc.identifier.urihttps://hdl.handle.net/10133/5664
dc.language.isoen_USen_US
dc.proquest.subjectComputer science [0984]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 Schienceen_US
dc.publisher.facultyArts and Scienceen_US
dc.relation.ispartofseriesThesis (University of Lethbridge. Faculty of Arts and Science)en_US
dc.subjectSoftware engineeringen_US
dc.subjectSystems engineeringen_US
dc.subjectText data miningen_US
dc.subjectNatural language processing
dc.subjectNatural language generation (Computer science)
dc.titleQuery-based summarization using reinforcement learning and transformer modelen_US
dc.typeThesisen_US
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