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Evaluation of Text Summaries Based on Linear Optimization of Content Metrics

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Evaluation of Text Summaries Based on Linear Optimization of Content Metrics/ by Jonathan Rojas-Simon, Yulia Ledeneva, Rene Arnulfo Garcia-Hernandez.
作者:	Rojas-Simon, Jonathan.
其他作者:	Garcia-Hernandez, Rene Arnulfo.
面頁冊數:	XV, 213 p. 57 illus., 11 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Big Data. -
電子資源:	https://doi.org/10.1007/978-3-031-07214-7
ISBN:	9783031072147

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. - 1st ed. 2022. - XV, 213 p. 57 illus., 11 illus. in color.online resource. - Studies in Computational Intelligence,10481860-9503 ;. - Studies in Computational Intelligence,564.

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

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

1017136
Big Data.

LC Class. No.: Q342

Dewey Class. No.: 006.3

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