國立高雄大學圖資館 |

Evaluation of text summaries based on linear optimization of content metrics

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Evaluation of text summaries based on linear optimization of content metricsby Jonathan Rojas-Simon, Yulia Ledeneva, Rene Arnulfo Garcia-Hernandez.
作者:	Rojas-Simon, Jonathan.
其他作者:	Ledeneva, Yulia.
出版者:	Cham :Springer International Publishing :2022.
面頁冊數:	xv, 213 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Automatic abstractingEvaluation.
電子資源:	https://doi.org/10.1007/978-3-031-07214-7
ISBN:	9783031072147$q(electronic bk.)

Evaluation of text summaries based on linear optimization of content metrics
Rojas-Simon, Jonathan.

Evaluation of text summaries based on linear optimization of content metrics[electronic resource] /by Jonathan Rojas-Simon, Yulia Ledeneva, Rene Arnulfo Garcia-Hernandez. - Cham :Springer International Publishing :2022. - xv, 213 p. :ill., digital ;24 cm. - Studies in computational intelligence,v. 10481860-9503 ;. - Studies in computational intelligence ;v. 216..

Introduction -- Background of the ETS -- Fundamentals of the ETS -- State-of-the-art Automatic Evaluation Methods -- A Novel Methodology based on Linear Optimization of Metrics for the ETS -- Experimenting with Linear Optimization of Metrics for Single-document Summarization Evaluation -- Experimenting with Linear Optimization of Metrics for Multi-document Summarization Evaluation -- Conclusions and future considerations for the ETS.

This book provides a comprehensive discussion and new insights about linear optimization of content metrics to improve the automatic Evaluation of Text Summaries (ETS) The reader is first introduced to the background and fundamentals of the ETS. Afterward, state-of-the-art evaluation methods that require or do not require human references are described. Based on how linear optimization has improved other natural language processing tasks, we developed a new methodology based on genetic algorithms that optimize content metrics linearly. Under this optimization, we propose SECO-SEVA as an automatic evaluation metric available for research purposes. Finally, the text finishes with a consideration of directions in which automatic evaluation could be improved in the future. The information provided in this book is self-contained. Therefore, the reader does not require an exhaustive background in this area. Moreover, we consider this book the first one that deals with the ETS in depth.

ISBN: 9783031072147$q(electronic bk.)

Standard No.: 10.1007/978-3-031-07214-7doiSubjects--Topical Terms:

933751
Automatic abstracting
--Evaluation.

LC Class. No.: Z695.92 / .R65 2022

Dewey Class. No.: 025.410285

Evaluation of text summaries based on linear optimization of content metrics
LDR:02549nmm a2200325 a 4500 001 629160
003 DE-He213
005 20220819190816.0
007 cr nn 008maaau
008 230224s2022 sz s 0 eng d
020 $a 9783031072147$q(electronic bk.)
020 $a 9783031072130$q(paper)
024 7 $a 10.1007/978-3-031-07214-7 $2 doi
035 $a 978-3-031-07214-7
040 $a GP $c GP
041 0 $a eng
050 4 $a Z695.92 $b .R65 2022
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 025.410285 $2 23
090 $a Z695.92 $b .R741 2022
100 1 $a Rojas-Simon, Jonathan. $3 933748
245 1 0 $a Evaluation of text summaries based on linear optimization of content metrics $h [electronic resource] / $c by Jonathan Rojas-Simon, Yulia Ledeneva, Rene Arnulfo Garcia-Hernandez.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a xv, 213 p. : $b ill., digital ; $c 24 cm.
490 1 $a Studies in computational intelligence, $x 1860-9503 ; $v v. 1048
505 0 $a Introduction -- Background of the ETS -- Fundamentals of the ETS -- State-of-the-art Automatic Evaluation Methods -- A Novel Methodology based on Linear Optimization of Metrics for the ETS -- Experimenting with Linear Optimization of Metrics for Single-document Summarization Evaluation -- Experimenting with Linear Optimization of Metrics for Multi-document Summarization Evaluation -- Conclusions and future considerations for the ETS.
520 $a This book provides a comprehensive discussion and new insights about linear optimization of content metrics to improve the automatic Evaluation of Text Summaries (ETS) The reader is first introduced to the background and fundamentals of the ETS. Afterward, state-of-the-art evaluation methods that require or do not require human references are described. Based on how linear optimization has improved other natural language processing tasks, we developed a new methodology based on genetic algorithms that optimize content metrics linearly. Under this optimization, we propose SECO-SEVA as an automatic evaluation metric available for research purposes. Finally, the text finishes with a consideration of directions in which automatic evaluation could be improved in the future. The information provided in this book is self-contained. Therefore, the reader does not require an exhaustive background in this area. Moreover, we consider this book the first one that deals with the ETS in depth.
650 0 $a Automatic abstracting $x Evaluation. $3 933751
650 0 $a Natural language processing (Computer science) $3 200539
650 0 $a Mathematical optimization. $3 183292
650 1 4 $a Computational Intelligence. $3 338479
650 2 4 $a Data Engineering. $3 839346
650 2 4 $a Big Data. $3 760530
700 1 $a Ledeneva, Yulia. $3 933749
700 1 $a Garcia-Hernandez, Rene Arnulfo. $3 933750
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Studies in computational intelligence ; $v v. 216. $3 380871
856 4 0 $u https://doi.org/10.1007/978-3-031-07214-7
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)