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Applying Natural Language Generation to Indicative Summarization

Min-Yen Kan·Kathleen R. McKeown·Judith L. Klavans
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Abstract

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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Min-Yen Kan, Kathleen R. McKeown, Judith L. Klavans. 2001-07-16. Applying Natural Language Generation to Indicative Summarization. https://arxiv.org/abs/cs/0107019

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