Applying regression models to query-focused multi-document summarization

You Ouyang, Wenjie Li, Sujian Li, Qin Lu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

98 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)227-237
Number of pages11
JournalInformation Processing and Management
Volume47
Issue number2
DOIs
Publication statusPublished - 1 Mar 2011

Keywords

  • Query-focused summarization
  • Support Vector Regression
  • Training data construction

ASJC Scopus subject areas

  • Information Systems
  • Media Technology
  • Computer Science Applications
  • Management Science and Operations Research
  • Library and Information Sciences

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