000 | 03788nam a22005415i 4500 | ||
---|---|---|---|
001 | 978-981-13-8934-4 | ||
003 | DE-He213 | ||
005 | 20240423125130.0 | ||
007 | cr nn 008mamaa | ||
008 | 190813s2019 si | s |||| 0|eng d | ||
020 |
_a9789811389344 _9978-981-13-8934-4 |
||
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 _2thema |
|
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 | ||
942 | _cSPRINGER | ||
999 |
_c174588 _d174588 |