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


This paper presents the use of Support Vector Machines (SVM) to detect relevant information to be included in a query-focused 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.