03484nam 22005895 450 991058859680332120230810175436.03-031-07214-610.1007/978-3-031-07214-7(MiAaPQ)EBC7076025(Au-PeEL)EBL7076025(CKB)24723836600041(DE-He213)978-3-031-07214-7(PPN)264191714(EXLCZ)992472383660004120220818d2022 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierEvaluation of Text Summaries Based on Linear Optimization of Content Metrics /by Jonathan Rojas-Simon, Yulia Ledeneva, Rene Arnulfo Garcia-Hernandez1st ed. 2022.Cham :Springer International Publishing :Imprint: Springer,2022.1 online resource (222 pages)Studies in Computational Intelligence,1860-9503 ;1048Print version: Rojas-Simon, Jonathan Evaluation of Text Summaries Based on Linear Optimization of Content Metrics Cham : Springer International Publishing AG,c2022 9783031072130 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.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.Studies in Computational Intelligence,1860-9503 ;1048Computational intelligenceEngineeringData processingBig dataComputational IntelligenceData EngineeringBig DataComputational intelligence.EngineeringData processing.Big data.Computational Intelligence.Data Engineering.Big Data.519.3025.410285Rojas-Simon Jonathan1253939MiAaPQMiAaPQMiAaPQBOOK9910588596803321Evaluation of Text Summaries Based on Linear Optimization of Content Metrics2907782UNINA