TitleApplying regression models to query-focused multi-document summarization
AuthorsOuyang, You
Li, Wenjie
Li, Sujian
Lu, Qin
AffiliationHong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China.
Peking Univ, Minist Educ, Key Lab Computat Linguist, Beijing, Peoples R China.
KeywordsQuery-focused summarization
Support Vector Regression
Training data construction
Issue Date2011
Publisherinformation processing management
CitationINFORMATION PROCESSING & MANAGEMENT.2011,47,(2),227-237.
AbstractMost 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. (C) 2010 Elsevier Ltd. All rights reserved.
Appears in Collections:计算语言学教育部重点实验室

Web of Science®

Checked on Last Week


Checked on Current Time


Checked on Current Time

Google Scholar™

License: See PKU IR operational policies.