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

Autor
Fuentes, M.; Alfonseca, E.; Rodriguez, H.
Tipus d'activitat
Document cientificotècnic
Data
2007-01
Codi
LSI-06-42-R
Repositori
http://hdl.handle.net/2117/87424 Obrir en finestra nova
Resum
This paper presents the use of Support Vector Machines (SVM) to detect relevant information to be included in a queryfocused summary. Several classifiers are trained using pyramids of summary content units information. The Mapping-Convergence algorithm is used with positive, unlabeled data, and a small set of negative seeds. The SVMs are tested on two Document Understanding Conference (DUC) 2006 systems. The performance of the new approaches is compared with the original systems using the DUC 20...
Citació
Fuentes, M., Alfonseca, E., Rodríguez, H. "Support vector machines for query-focused summarization trained and evaluated on pyramid data". 2007.
Paraules clau
Machine learning, Text summarization
Grup de recerca
GPLN - Grup de Processament del Llenguatge Natural
IDEAI-UPC Intelligent Data Science and Artificial Intelligence
TALP - Centre de Tecnologies i Aplicacions del Llenguatge i la Parla

Participants

Arxius