Re-ranking summaries based on cross-document information extraction

Heng Ji, Juan Liu, Benoit Favre, Dan Gillick, Dilek Hakkani-Tur

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

Abstract

This paper describes a novel approach of improving multi-document summarization based on cross-document information extraction (IE). We describe a method to automatically incorporate IE results into sentence ranking. Experiments have shown our integration methods can significantly improve a high-performing multi-document summarization system, according to the ROUGE-2 and ROUGE-SU4 metrics (7.38% relative improvement on ROUGE-2 recall), and the generated summaries are preferred by human subjects (0.78 higher TAC Content score and 0.11 higher Readability/Fluency score).

Original languageEnglish (US)
Title of host publicationInformation Retrieval Technology - 6th Asia Information Retrieval Societies Conference, AIRS 2010, Proceedings
PublisherSpringer
Pages432-442
Number of pages11
ISBN (Print)3642171869, 9783642171864
DOIs
StatePublished - 2010
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6458 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • Information Extraction
  • Multi-document Summarization

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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