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024 7 _a10.1007/978-981-13-8934-4
_2doi
050 4 _aTK7885-7895
050 4 _aTA169-169.3
072 7 _aUK
_2bicssc
072 7 _aCOM067000
_2bisacsh
072 7 _aUK
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082 0 4 _a004.24
_223
100 1 _aMehta, Parth.
_eauthor.
_0(orcid)0000-0002-4509-1298
_1https://orcid.org/0000-0002-4509-1298
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aFrom Extractive to Abstractive Summarization: A Journey
_h[electronic resource] /
_cby Parth Mehta, Prasenjit Majumder.
250 _a1st ed. 2019.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2019.
300 _aXI, 116 p. 470 illus., 9 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aIntroduction.-Related Work -- Corpora and Evaluation for Text Summarization -- Domain Specific Summarization -- Improving sentence extraction through rank aggregation -- Leveraging content similarity in summaries for generating better ensembles.-Neural model for sentence compression -- Conclusion.
520 _aThis book describes recent advances in text summarization, identifies remaining gaps and challenges, and proposes ways to overcome them. It begins with one of the most frequently discussed topics in text summarization – ‘sentence extraction’ –, examines the effectiveness of current techniques in domain-specific text summarization, and proposes several improvements. In turn, the book describes the application of summarization in the legal and scientific domains, describing two new corpora that consist of more than 100 thousand court judgments and more than 20 thousand scientific articles, with the corresponding manually written summaries. The availability of these large-scale corpora opens up the possibility of using the now popular data-driven approaches based on deep learning. The book then highlights the effectiveness of neural sentence extraction approaches, which perform just as well as rule-based approaches, but without the need for any manual annotation. As a next step, multiple techniques for creating ensembles of sentence extractors – which deliver better and more robust summaries – are proposed. In closing, the book presents a neural network-based model for sentence compression. Overall the book takes readers on a journey that begins with simple sentence extraction and ends in abstractive summarization, while also covering key topics like ensemble techniques and domain-specific summarization, which have not been explored in detail prior to this.
650 0 _aComputers.
650 0 _aComputer networks .
650 0 _aApplication software.
650 1 4 _aHardware Performance and Reliability.
650 2 4 _aComputer Communication Networks.
650 2 4 _aComputer and Information Systems Applications.
700 1 _aMajumder, Prasenjit.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789811389337
776 0 8 _iPrinted edition:
_z9789811389351
776 0 8 _iPrinted edition:
_z9789811389368
856 4 0 _uhttps://doi.org/10.1007/978-981-13-8934-4
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
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