Multi-document summarisation using generic relation extraction

Ben Hachey*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

13 Citations (Scopus)

Abstract

Experiments are reported that investigate the effect of various source document representations on the accuracy of the sentence extraction phase of a multi-document summarisation task. A novel representation is introduced based on generic relation extraction (GRE), which aims to build systems for relation identification and characterisation that can be transferred across domains and tasks without modification of model parameters. Results demonstrate performance that is significantly higher than a non-trivial baseline that uses tf*idf -weighted words and at least as good as a comparable but less general approach from the literature. Analysis shows that the representations compared are complementary, suggesting that extraction performance could be further improved through system combination.

Original languageEnglish
Title of host publicationEMNLP 2009 - Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: A Meeting of SIGDAT, a Special Interest Group of ACL, Held in Conjunction with ACL-IJCNLP 2009
Pages420-429
Number of pages10
Publication statusPublished - 2009
Event2009 Conference on Empirical Methods in Natural Language Processing, EMNLP 2009, Held in Conjunction with ACL-IJCNLP 2009 - Singapore, Singapore
Duration: 6 Aug 20097 Aug 2009

Other

Other2009 Conference on Empirical Methods in Natural Language Processing, EMNLP 2009, Held in Conjunction with ACL-IJCNLP 2009
CountrySingapore
CitySingapore
Period6/08/097/08/09

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