Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/22254
Title: Applying regression models to query-focused multi-document summarization
Authors: Ouyang, Y
Li, W 
Li, S
Lu, Q
Keywords: Query-focused summarization
Support Vector Regression
Training data construction
Issue Date: 2011
Publisher: Pergamon Press
Source: Information processing and management, 2011, v. 47, no. 2, p. 227-237 How to cite?
Journal: Information processing and management 
Abstract: Most existing research on applying machine learning techniques to document summarization explores either classification models or learning-to-rank models. This paper presents our recent study on how to apply a different kind of learning models, namely regression models, to query-focused multi-document summarization. We choose to use Support Vector Regression (SVR) to estimate the importance of a sentence in a document set to be summarized through a set of pre-defined features. In order to learn the regression models, we propose several methods to construct the "pseudo" training data by assigning each sentence with a "nearly true" importance score calculated with the human summaries that have been provided for the corresponding document set. A series of evaluations on the DUC data sets are conducted to examine the efficiency and the robustness of the proposed approaches. When compared with classification models and ranking models, regression models are consistently preferable.
URI: http://hdl.handle.net/10397/22254
ISSN: 0306-4573
DOI: 10.1016/j.ipm.2010.03.005
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