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Applying natural language generation to indicative summarization

Klavans, Judith L.; Kan, Min-yen; McKeown, Kathleen

The task of creating indicative summaries that help a searcher decide whether to read a particular document is a difficult task. This paper examines the indicative summarization task from a generation perspective, by first analyzing its required content via published guidelines and corpus analysis. We show how these summaries can be factored into a set of document features, and how an implemented content planner uses the topicality document feature to create indicative multidocument query-based summaries.

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More About This Work

Academic Units
Computer Science
Publisher
Proceedings of 8th European Workshop on Natural Language Generation at the ACL/EACL 2001 Conference
Published Here
May 3, 2013
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