Evaluation of Text Summaries Based on Linear Optimization of Content Metrics

Jonathan Rojas-Simon

ISBN 10: 3031072138 / ISBN 13: 9783031072130

Published by Springer International Publishing Aug 2022, 2022

About this Item

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.

Seller Inventory # 9783031072130

Bibliographic Details

• Title: Evaluation of Text Summaries Based on Linear Optimization of Content Metrics
• Publisher: Springer International Publishing Aug 2022
• Publication Date: 2022
• Language: English
• ISBN 10: 3031072138
• ISBN 13: 9783031072130
• Binding: Buch
• Condition: Neu

About this title

Synopsis

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.