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<Paper uid="W03-1101">
  <Title>Improving Summarization Performance by Sentence Compression - A Pilot Study</Title>
  <Section position="8" start_page="0" end_page="0" type="concl">
    <SectionTitle>
7 Conclusions
</SectionTitle>
    <Paragraph position="0"> In this paper we presented an empirical study of the effectiveness of applying sentence compression to improve summarization performance. We used a good sentence compression algorithm, compared the performance of five different ranking algorithms, and found that pure a-sentence-at-a-time syntactic or shallow semantic-based reranking was not enough to boost system performance. However, the significant difference between the ORACLE run and the original run (ORG) indicated there is potential in sentence compression but we need to find a better compression selection function that should take into account global cross-sentence optimization. This indicated local optimization at the sentence level such as Knight and Marcu's (2000) noisy-channel model is not enough when our goal is to find the best compressed summaries not the best compressed sentences. In the future, we would like to apply a similar methodology to different text units, for example, sub-sentence units such as elementary discourse unit (Marcu, 1999) and a larger corpus, for example, DUC 2002 and DUC 2003. We want to explore compression techniques to go beyond simple sentence extraction.</Paragraph>
  </Section>
class="xml-element"></Paper>