Combining state-of-the-art models for multi-document summarization using maximal marginal relevance

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dc.contributor.authorAdams, David
dc.contributor.authorUniversity of Lethbridge. Faculty of Arts and Science
dc.contributor.supervisorChali, Yllias
dc.date.accessioned2021-09-16T22:44:09Z
dc.date.available2021-09-16T22:44:09Z
dc.date.issued2021
dc.degree.levelMastersen_US
dc.description.abstractIn Natural Language Processing, multi-document summarization (MDS) poses many challenges to researchers. While advancements in deep learning approaches have led to the development of several advanced language models capable of summarization, the variety of approaches specific to the problem of multi-document summarization remains relatively limited. Current state-of-the-art models produce impressive results on multi-document datasets, but the question of whether improvements can be made via the combination of these state-of-the-art models remains. This question is particularly relevant in few-shot and zero-shot applications, in which models have little familiarity or no familiarity with the expected output, respectively. To explore one potential method, we implement a query-relevance-focused approach which combines the pretrained models' outputs using maximal marginal relevance (MMR). Our MMR-based approach shows improvement over some aspects of the current state-of-the-art results while preserving overall state-of-the-art performance, with larger improvements occurring in fewer-shot contexts.en_US
dc.description.sponsorshipUniversity of Lethbridgeen_US
dc.identifier.urihttps://hdl.handle.net/10133/6026
dc.language.isoen_USen_US
dc.proquest.subjectComputer science [0984]en_US
dc.proquest.subjectArtificial intelligence [0800]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)en_US
dc.subjectResearch Subject Categories::TECHNOLOGY::Information technology::Computer science::Computer scienceen_US
dc.subjectnatural language processingen_US
dc.subjectsummarizationen_US
dc.subjectmulti-document summarizationen_US
dc.subjectmaximal marginal relevanceen_US
dc.subjectmachine learningen_US
dc.subjectArtificial intelligenceen_US
dc.subjectAutomatic abstractingen_US
dc.subjectElectronic information resources -- Abstracting and indexing.en_US
dc.subjectInformation storage and retrieval systems.en_US
dc.subjectNatural language processingen_US
dc.subjectSelective dissemination of informationen_US
dc.titleCombining state-of-the-art models for multi-document summarization using maximal marginal relevanceen_US
dc.typeThesisen_US
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