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<Paper uid="W01-0813">
  <Title>Applying Natural Language Generation to Indicative Summarization</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> 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.</Paragraph>
    <Paragraph position="1"> 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.</Paragraph>
  </Section>
class="xml-element"></Paper>