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<Paper uid="W06-1643">
  <Title>Sydney, July 2006. c(c)2006 Association for Computational Linguistics A Skip-Chain Conditional Random Field for Ranking Meeting Utterances by Importance[?]</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We describe a probabilistic approach to content selection for meeting summarization. We use skip-chain Conditional Random Fields (CRF) to model non-local pragmatic dependencies between paired utterances such as QUESTION-ANSWER that typically appear together in summaries, and show that these models outperform linear-chain CRFs and Bayesian models in the task. We also discuss different approaches for ranking all utterances in a sequence using CRFs. Our best performing system achieves 91.3% of human performance when evaluated with the Pyramid evaluation metric, which represents a 3.9% absolute increase compared to our most competitive non-sequential classifier.</Paragraph>
  </Section>
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