From Extractive to Abstractive Summarization A Journey
| dc.contributor.guide | Majumder, Prasenjit | |
| dc.coverage.spatial | ||
| dc.creator.researcher | Mehta, Parth | |
| dc.date.accessioned | 2019-01-24T12:22:08Z | |
| dc.date.available | 2019-01-24T12:22:08Z | |
| dc.date.awarded | ||
| dc.date.completed | 2018 | |
| dc.date.registered | 3-6-2013 | |
| dc.description.abstract | Research 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.accompanyingmaterial | CD | |
| dc.format.dimensions | ||
| dc.format.extent | x, 112p. | |
| dc.identifier.uri | http://hdl.handle.net/10603/226791 | |
| dc.language | English | |
| dc.publisher.institution | Department of Information and Communication Technology | |
| dc.publisher.place | Gandhinagar | |
| dc.publisher.university | Dhirubhai Ambani Institute of Information and Communication Technology (DA-IICT) | |
| dc.relation | ||
| dc.rights | university | |
| dc.source.university | University | |
| dc.subject.keyword | Engineering and Technology,Computer Science,Computer Science Artificial Intelligence | |
| dc.title | From Extractive to Abstractive Summarization A Journey | |
| dc.title.alternative | ||
| dc.type.degree | Ph.D. |
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