國立高雄大學圖資館 |

Language: English

Back

From extractive to abstractive summa...

Majumder, Prasenjit.

From extractive to abstractive summarizationa journey /

Record Type:	Electronic resources : Monograph/item
Title/Author:	From extractive to abstractive summarizationby Parth Mehta, Prasenjit Majumder.
Reminder of title:	a journey /
Author:	Mehta, Parth.
other author:	Majumder, Prasenjit.
Published:	Singapore :Springer Singapore :2019.
Description:	xi, 116 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Computational linguistics.
Online resource:	https://doi.org/10.1007/978-981-13-8934-4
ISBN:	9789811389344$q(electronic bk.)

From extractive to abstractive summarizationa journey /
Mehta, Parth.

From extractive to abstractive summarizationa journey /[electronic resource] :by Parth Mehta, Prasenjit Majumder. - Singapore :Springer Singapore :2019. - xi, 116 p. :ill., digital ;24 cm.

This 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.

ISBN: 9789811389344$q(electronic bk.)

Standard No.: 10.1007/978-981-13-8934-4doiSubjects--Topical Terms:

181250
Computational linguistics.

LC Class. No.: P98 / .M44 2019

Dewey Class. No.: 410.285

From extractive to abstractive summarizationa journey /
LDR:02478nmm a2200325 a 4500 001 586720
003 DE-He213
005 20200701221121.0
006 m d
007 cr nn 008maaau
008 210326s2019 si s 0 eng d
020 $a 9789811389344$q(electronic bk.)
020 $a 9789811389337$q(paper)
024 7 $a 10.1007/978-981-13-8934-4 $2 doi
035 $a 978-981-13-8934-4
040 $a GP $c GP
041 0 $a eng
050 4 $a P98 $b .M44 2019
072 7 $a UYD $2 bicssc
072 7 $a COM067000 $2 bisacsh
072 7 $a UYD $2 thema
072 7 $a UKR $2 thema
082 0 4 $a 410.285 $2 23
090 $a P98 $b .M498 2019
100 1 $a Mehta, Parth. $3 878271
245 1 0 $a From extractive to abstractive summarization $h [electronic resource] : $b a journey / $c by Parth Mehta, Prasenjit Majumder.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2019.
300 $a xi, 116 p. : $b ill., digital ; $c 24 cm.
520 $a This 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 $a Computational linguistics. $3 181250
650 0 $a Automatic abstracting. $3 257585
650 1 4 $a Performance and Reliability. $3 277564
650 2 4 $a Computer Communication Networks. $3 218087
650 2 4 $a Information Systems Applications (incl. Internet) $3 530743
700 1 $a Majumder, Prasenjit. $3 805319
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-13-8934-4
950 $a Computer Science (SpringerNature-11645)