Evaluation of Text Summaries Based on Linear Optim
Rojas-Simon, Jonathan; Ledeneva, Yulia; Garcia-Hernandez, Rene Arnulfo
ISBN 10: 3031072162 ISBN 13: 9783031072161
Editorial: Springer, 2023
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Descripción
Descripción:
pp. 232. N° de ref. del artículo 26398548998
Sinopsis:
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.
De la contraportada:
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.
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Detalles bibliográficos
Título: Evaluation of Text Summaries Based on Linear...
Editorial: Springer
Año de publicación: 2023
Encuadernación: Encuadernación de tapa blanda
Condición: New
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Evaluation of Text Summaries Based on Linear Optimization of Content Metrics
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Springer International Publishing, 2023
ISBN 10: 3031072162
ISBN 13: 9783031072161
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Kartoniert / Broschiert. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Introduces the reader to the background and fundamentals of the Evaluation of Text Summaries (ETS)Provides state-of-the-art studies and new methodologies for improving the ETSShows the design of experiments that combine evaluation metrics f. Nº de ref. del artículo: 959198236
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Evaluation of Text Summaries Based on Linear Optimization of Content Metrics (Studies in Computational Intelligence)
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Evaluation of Text Summaries Based on Linear Optimization of Content Metrics
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ISBN 10: 3031072162
ISBN 13: 9783031072161
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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Nº de ref. del artículo: 9783031072161
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Evaluation of Text Summaries Based on Linear Optimization of Content Metrics
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ISBN 10: 3031072162
ISBN 13: 9783031072161
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - 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. 232 pp. Englisch. Nº de ref. del artículo: 9783031072161
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Evaluation of Text Summaries Based on Linear Optimization of Content Metrics
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Springer International Publishing, Springer Nature Switzerland Aug 2023, 2023
ISBN 10: 3031072162
ISBN 13: 9783031072161
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Taschenbuch. Condición: Neu. 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 GmbH, Tiergartenstr. 17, 69121 Heidelberg 232 pp. Englisch. Nº de ref. del artículo: 9783031072161
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Evaluation of Text Summaries Based on Linear Optimization of Content Metrics (Studies in Computational Intelligence)
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Evaluation of Text Summaries Based on Linear Optim
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