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<Paper uid="P00-1038">
  <Title>Query-Relevant Summarization using FAQs</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> This paper introduces a statistical model for query-relevant summarization: succinctly characterizing the relevance of a document to a query. Learning parameter values for the proposed model requires a large collection of summarized documents, which we do not have, but as a proxy, we use a collection of FAQ (frequently-asked question) documents. Taking a learning approach enables a principled, quantitative evaluation of the proposed system, and the results of some initial experiments--on a collection of Usenet FAQs and on a FAQ-like set of customer-submitted questions to several large retail companies--suggest the plausibility of learning for summarization.</Paragraph>
  </Section>
class="xml-element"></Paper>