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<Paper uid="W06-0701">
  <Title>Sydney, July 2006. c(c)2006 Association for Computational Linguistics Dimensionality Reduction Aids Term Co-Occurrence Based Multi-Document Summarization</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> A key task in an extraction system for query-oriented multi-document summarisation, necessary for computing relevance and redundancy, is modelling text semantics. In the Embra system, we use a representation derived from the singular value decomposition of a term co-occurrence matrix. We present methods to show the reliability of performance improvements.</Paragraph>
    <Paragraph position="1"> We find that Embra performs better with dimensionality reduction.</Paragraph>
  </Section>
class="xml-element"></Paper>