Support Vector Machines for Query-focused Summarization trained and evaluated on Pyramid data

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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.

María Fuentes Fort, Enrique Alfonseca, Hora

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ACL 2007 | Computational Linguistics | Several SVMs | Summary Content Units | Support Vector Machines |

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Added	29 Oct 2010
Updated	29 Oct 2010
Type	Conference
Year	2007
Where	ACL
Authors	María Fuentes Fort, Enrique Alfonseca, Horacio Rodríguez

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Support Vector Machines for Query-focused Summarization trained and evaluated on Pyramid data

ACL 2007 | Computational Linguistics | Several SVMs | Summary Content Units | Support Vector Machines |

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