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<Paper uid="C00-1072">
  <Title>The Automated Acquisition of Topic Signatures for Text Summarization</Title>
  <Section position="8" start_page="499" end_page="499" type="concl">
    <SectionTitle>
'7 Conclusion
</SectionTitle>
    <Paragraph position="0"> In this paI)er we l)resented a t)rocedure to automati(:ally acquire topic signatures and valuated the eflk~ctiveness of applying tol)i(: signatures to extract tot)i(: relevant senten(:es against two other methods. The tot)ie signature method outt)erforms the baseline and the tfidfmethods for all test topics. Topic signatures can not only recognize related terms (topic identifi(:ation), but grout) related terms togetlmr under one target concept (topic interpretation). 'IbI)i(: identification and interpretation are two essential steps in a typical automated text summarization system as we l)resent in Section 3.</Paragraph>
    <Paragraph position="1"> '\]))pic: signatures (:an also been vie.wed as an inverse process of query expansion. Query expansion intends to alleviate the word mismatch ln'oblenl in infornmtion retrieval, since documents are normally written in different vocabulary, ttow to atttomati(ally identify highly e(nrelated terms and use them to improve information retrieval performance has been a main research issue since late 19611's. Recent advances in the query expansion (Xu and Croft, 1996) can also shed some light on the creation of topic signatures. Although we focus the ltse of topic signatures to aid text summarization in this paper, we plan to explore the possibility of applying topic signatures to perform query expansion in the future.</Paragraph>
    <Paragraph position="2"> The results reported are encouraging enough to allow us to contimm with topic signatures as the vehMe for a first approximation to worht knowledge.</Paragraph>
    <Paragraph position="3"> We are now busy creating a large nmnber of signature.s to overcome the world knowledge acquisition problem and use them in topic interpretation.</Paragraph>
  </Section>
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