Evaluation of Text Summaries Based on Linear Optimization of Content Metrics
Language: English
Published by Springer, Palgrave Macmillan Aug 2022, 2022
- Hardcover
- New
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This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 232 pp. Englisch.…
Seller Inventory # 9783031072130
- Title
- Evaluation of Text Summaries Based on Linear Optimization of Content Metrics
- Author
- Jonathan Rojas-Simon
- Publisher
- Springer, Palgrave Macmillan Aug 2022
- Publication year
- 2022
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 3031072138
- ISBN 13
- 9783031072130
- Item weight
- 518 grams
- Dimensions
- 241x160x19 mm
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.
"Synopsis" may belong to another edition of this title.
From the Back Cover
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.
"About the title" may belong to another edition of this title.
buchversandmimpf2000
Emtmannsberg, BAYE, Germany
AbeBooks seller since January 23, 2017
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