Support vector machines for query-focused summarization trained and evaluated on pyramid data

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To access the full text documents, please follow this link: http://hdl.handle.net/2117/13844

dc.contributor	Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics
dc.contributor	Universitat Politècnica de Catalunya. GPLN - Grup de Processament del Llenguatge Natural
dc.contributor.author	Fuentes Fort, Maria
dc.contributor.author	Alfonseca, Enrique
dc.contributor.author	Rodríguez Hontoria, Horacio
dc.date	2007
dc.identifier.citation	Fuentes, M.; Alfonseca, E.; Rodriguez, H. Support vector machines for query-focused summarization trained and evaluated on pyramid data. A: Annual Meeting of the Association for Computational Linguistics. "45th Annual Meeting of the Association for Computational Linguistics". Praga: 2007, p. 57-60.
dc.identifier.uri	http://hdl.handle.net/2117/13844
dc.language.iso	eng
dc.relation	http://www.aclweb.org/anthology/P07-2045
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Llenguatge natural
dc.subject	Multi-document summarization
dc.subject	Support vector machines
dc.subject	Resums -- Metodologia
dc.title	Support vector machines for query-focused summarization trained and evaluated on pyramid data
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	info:eu-repo/semantics/conferenceObject
dc.description.abstract	This paper presents the use of Support Vector Machines (SVM) to detect relevant information to be included in a queryfocused summary. Several SVMs are trained using information from pyramids of summary content units. Their performance is compared with the best performing systems in DUC-2005, using both ROUGE and autoPan, an automatic scoring method for pyramid evaluation.
dc.description.abstract	Peer Reviewed

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