001448890 000__ 03552cam\a2200541\i\4500
001448890 001__ 1448890
001448890 003__ OCoLC
001448890 005__ 20230310004302.0
001448890 006__ m\\\\\o\\d\\\\\\\\
001448890 007__ cr\cn\nnnunnun
001448890 008__ 220825s2022\\\\sz\a\\\\o\\\\\000\0\eng\d
001448890 019__ $$a1341985848
001448890 020__ $$a9783031072147$$q(electronic bk.)
001448890 020__ $$a3031072146$$q(electronic bk.)
001448890 020__ $$z9783031072130
001448890 020__ $$z3031072138
001448890 0247_ $$a10.1007/978-3-031-07214-7$$2doi
001448890 035__ $$aSP(OCoLC)1342111106
001448890 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dYDX$$dEBLCP$$dOCLCQ$$dOCL
001448890 049__ $$aISEA
001448890 050_4 $$aZ695.92
001448890 08204 $$a025.4/10285$$223/eng/20220825
001448890 1001_ $$aRojas-Simon, Jonathan,$$eauthor.
001448890 24510 $$aEvaluation of text summaries based on linear optimization of content metrics /$$cJonathan Rojas-Simon, Yulia Ledeneva, Rene Arnulfo Garcia-Hernandez.
001448890 264_1 $$aCham, Switzerland :$$bSpringer,$$c2022.
001448890 300__ $$a1 online resource :$$billustrations (black and white, and colour).
001448890 336__ $$atext$$btxt$$2rdacontent
001448890 337__ $$acomputer$$bc$$2rdamedia
001448890 338__ $$aonline resource$$bcr$$2rdacarrier
001448890 4901_ $$aStudies in computational intelligence ;$$vvolume 1048
001448890 5050_ $$aIntroduction -- 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.
001448890 506__ $$aAccess limited to authorized users.
001448890 520__ $$aThis 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.
001448890 588__ $$aDescription based on print version record.
001448890 650_0 $$aAutomatic abstracting$$xEvaluation.
001448890 650_0 $$aNatural language processing (Computer science)
001448890 650_0 $$aMathematical optimization.
001448890 655_0 $$aElectronic books.
001448890 7001_ $$aLedeneva, Yulia,$$eauthor.
001448890 7001_ $$aGarcia-Hernandez, Rene Arnulfo,$$eauthor.
001448890 77608 $$iPrint version:$$aRojas-Simon, Jonathan.$$tEvaluation of text summaries based on linear optimization of content metrics.$$dCham : Springer, 2022$$z9783031072130$$w(OCoLC)1328017354
001448890 830_0 $$aStudies in computational intelligence ;$$vv. 1048.
001448890 852__ $$bebk
001448890 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-07214-7$$zOnline Access$$91397441.1
001448890 909CO $$ooai:library.usi.edu:1448890$$pGLOBAL_SET
001448890 980__ $$aBIB
001448890 980__ $$aEBOOK
001448890 982__ $$aEbook
001448890 983__ $$aOnline
001448890 994__ $$a92$$bISE