Towards CST-enhanced summarization

Authors:
Zhu Zhang;Sasha Blair-Goldensohn;Dragomir R. Radev
Affiliations:
School of Information, University of Michigan, Ann Arbor, MI;School of Information, University of Michigan, Ann Arbor, MI;School of Information and Department of EECS, University of Michigan, Ann Arbor, MI
Venue:
Eighteenth national conference on Artificial intelligence
Year:
2002

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Abstract

In this paper, we propose to enhance the process of automatic extractive multi-document text summarization by taking into account cross-document structural relationships as posited in Cross-document Structure Theory (CST). An arbitrary multidocument extract can be CST-enhanced by replacing low-salience sentences with other sentences that increase the total number of CST relationships included in the summary. We show that CST-enhanced summaries outperform their unmodified counterparts using the relative utility evaluation metric. We also show that the effect of a CST relationship on an extract depends on its type.