Generic multi-document summarization using topic-oriented information

Yulong Pei, Wenpeng Yin, Lian'En Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations


The graph-based ranking models have been widely used for multi-document summarization recently. By utilizing the correlations between sentences, the salient sentences can be extracted according to the ranking scores. However, sentences are treated in a uniform way without considering the topic-level information in traditional methods. This paper proposes the topic-oriented PageRank (ToPageRank) model, in which topic information is fully incorporated, and the topic-oriented HITS (ToHITS) model is designed to compare the influence of different graph-based algorithms. We choose the DUC2004 data set to examine the models. Experimental results demonstrate the effectiveness of ToPageRank. And the results also show that ToPageRank is more effective and robust than other models including ToHIST under different evaluation metrics.

Original languageEnglish (US)
Title of host publicationPRICAI 2012
Subtitle of host publicationTrends in Artificial Intelligence - 12th Pacific Rim International Conference on Artificial Intelligence, Proceedings
Number of pages12
StatePublished - 2012
Event12th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2012 - Kuching, Malaysia
Duration: Sep 3 2012Sep 7 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7458 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference12th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2012

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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