Evaluation of Text Summaries Based on Linear Optimization of Content Metrics: 1048 (Studies in Computational Intelligence, 1048) von Rojas-Simon, Jonathan/ Ledeneva, Yulia/ Garcia-Hernandez, Rene Arnulfo: Brand New Paperback (2023)

Beschreibung

228 pages. 9.25x6.10x0.49 inches. In Stock.

Bestandsnummer des Verk�ufers x-3031072162

Verk�ufer kontaktieren

Diesen Artikel melden

Bibliografische Details

Titel: Evaluation of Text Summaries Based on Linear Optimization of Content Metrics: 1048 (Studies in Computational Intelligence, 1048)
Verlag: Springer
Erscheinungsjahr: 2023
Sprache: Englisch
ISBN-10: 3031072162
ISBN-13: 9783031072161
Einband: Paperback
Zustand: Brand New

�ber diesen Titel

Inhaltsangabe

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.

Von der hinteren Coverseite

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.

��ber diesen Titel� kann sich auf eine andere Ausgabe dieses Titels beziehen.