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<Paper uid="W99-0625">
  <Title>Normalized? Yes Yes Yes No Yes No Yes Yes Yes Yes Yes Yes</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We present a new composite similarity metric that combines information from multiple linguistic indicators to measure semantic distance between pairs of small textual units. Several potential features are investigated and an optireal combination is selected via machine learning. We discuss a more restrictive definition of similarity than traditional, document-level and information retrieval-oriented, notions of similarity, and motivate it by showing its relevance to the multi-document text summarization problem. Results from our system are evaluated against standard information retrieval techniques, establishing that the new method is more effective in identifying closely related textual units.</Paragraph>
  </Section>
class="xml-element"></Paper>