UID:
almahu_9949357541302882
Format:
XV, 213 p. 57 illus., 11 illus. in color.
,
online resource.
Edition:
1st ed. 2022.
ISBN:
9783031072147
Series Statement:
Studies in Computational Intelligence, 1048
Content:
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.
Note:
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.
In:
Springer Nature eBook
Additional Edition:
Printed edition: ISBN 9783031072130
Additional Edition:
Printed edition: ISBN 9783031072154
Additional Edition:
Printed edition: ISBN 9783031072161
Language:
English
DOI:
10.1007/978-3-031-07214-7
URL:
https://doi.org/10.1007/978-3-031-07214-7
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