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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="8" start_page="3" end_page="5" type="concl">
    <SectionTitle>
6 Conclusions
</SectionTitle>
    <Paragraph position="0"> We have presented experiments with query-oriented multi-document summarisation. The experiments explore the question of whether SVD dimensionality reduction offers any improvement over a term co-occurrence representation for sentence semantics for measuring relevance and redundancy. While the experiments show that our system does not outperform a term x sentence tf.idf system, we have shown that the SVD reduced representation of a term co-occurrence space built from a large corpora performs better than the unreduced representation. This contra- null dicts related work where SVD did not provide an improvement over unreduced representations on the name discrimination task (Pedersen et al., 2005). However, it is compatible with other work where SVD has been shown to help on the task of estimating human notions of word similarity (Matveeva et al., 2005; Rohde et al., In prep).</Paragraph>
    <Paragraph position="1"> A detailed analysis using the Friedman test and a cascade of Wilcoxon signed ranks tests suggest that our results are statistically valid despite the unreliability of the Rouge evaluation metric due to its low variance across systems.</Paragraph>
  </Section>
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