Evaluation of text summaries based on linear optimization of content metrics / Jonathan Rojas-Simon, Yulia Ledeneva, Rene Arnulfo Garcia-Hernandez.
Rojas-Simon, Jonathan, author.; Ledeneva, Yulia, author.; Garcia-Hernandez, Rene Arnulfo, author.
2022
Z695.92
Available Online

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Title Evaluation of text summaries based on linear optimization of content metrics / Jonathan Rojas-Simon, Yulia Ledeneva, Rene Arnulfo Garcia-Hernandez.
Author Rojas-Simon, Jonathan, author.
ISBN 9783031072147 (electronic bk.)
3031072146 (electronic bk.)
9783031072130
3031072138
DOI https://doi.org/10.1007/978-3-031-07214-7
Published Cham, Switzerland : Springer, 2022.
Language English
Description 1 online resource : illustrations (black and white, and colour).
Other Standard Identifiers 10.1007/978-3-031-07214-7 doi
Call Number Z695.92
Dewey Decimal Classification 025.4/10285
Summary 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.
Access Note Access limited to authorized users.
Source of Description Description based on print version record.
Added Author Ledeneva, Yulia, author.
Garcia-Hernandez, Rene Arnulfo, author.
Series Studies in computational intelligence ; v. 1048.
Available in Other Form Evaluation of text summaries based on linear optimization of content metrics.
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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.

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