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Support vector machines for query-focused summarization trained and evaluated on pyramid data
Fuentes Fort, Maria; Alfonseca, Enrique; Rodríguez Hontoria, Horacio
Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics; Universitat Politècnica de Catalunya. GPLN - Grup de Processament del Llenguatge Natural
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.
Peer Reviewed
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Llenguatge natural
Multi-document summarization
Support vector machines
Resums -- Metodologia
info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/conferenceObject
         

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