Exploring strategies for developing link analysis based question-oriented multi-document summarization models

Su Jian Li, Wei Wang, Wenjie Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Graph ranking algorithms have been successfully used in multi-document summarization. Among them, the basic link analysis model has drawn much attention due to its' mutual reinforcement principle which appears to be sound for the generic summarization task. In this paper, we explore effective strategies for extending the basic link analysis model to question-oriented multi-document summarization. Three kinds of strategies, namely link re-weighting, baseset downsizing and projection, are proposed to introduce question-dependent similarity metric, adjust the node number and refine the ranking process respectively. Experimental results evaluated on the DUC data sets demonstrate that these three strategies can achieve better results.
Original languageEnglish
Title of host publicationProceedings of 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011
Pages1896-1901
Number of pages6
Volume4
DOIs
Publication statusPublished - 7 Nov 2011
Event2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011 - Guilin, Guangxi, China
Duration: 10 Jul 201113 Jul 2011

Conference

Conference2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011
CountryChina
CityGuilin, Guangxi
Period10/07/1113/07/11

Keywords

  • Baseset Downsizing
  • Link Analysis Model
  • Link Re-weighting
  • Projection
  • Question-oriented Multi-document Summarization

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Human-Computer Interaction

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