From Extractive to Abstractive Summarization A Journey

dc.contributor.guideMajumder, Prasenjit
dc.coverage.spatial
dc.creator.researcherMehta, Parth
dc.date.accessioned2019-01-24T12:22:08Z
dc.date.available2019-01-24T12:22:08Z
dc.date.awarded
dc.date.completed2018
dc.date.registered3-6-2013
dc.description.abstractResearch in the field of text summarisation has primarily been dominated by investigations of various sentence extraction techniques with a significant focus towards news articles. In this thesis, we intend to look beyond generic sentence extraction and instead focus on domain-specific summarisation, methods for creating ensembles of multiple extractive summarisation techniques and using sentence compression as the first step towards abstractive summarisation. Our proposed approach based on attention-based neural network learns to automatically identify these key phrases from pseudo-labelled data, without requiring any annotation or handcrafted rules. The proposed model outperforms existing baselines and state of the art systems by a large margin. newline
dc.description.note
dc.format.accompanyingmaterialCD
dc.format.dimensions
dc.format.extentx, 112p.
dc.identifier.urihttp://hdl.handle.net/10603/226791
dc.languageEnglish
dc.publisher.institutionDepartment of Information and Communication Technology
dc.publisher.placeGandhinagar
dc.publisher.universityDhirubhai Ambani Institute of Information and Communication Technology (DA-IICT)
dc.relation
dc.rightsuniversity
dc.source.universityUniversity
dc.subject.keywordEngineering and Technology,Computer Science,Computer Science Artificial Intelligence
dc.titleFrom Extractive to Abstractive Summarization A Journey
dc.title.alternative
dc.type.degreePh.D.

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