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<Paper uid="W06-3407">
  <Title>Topic Segmentation of Dialogue</Title>
  <Section position="11" start_page="48" end_page="48" type="concl">
    <SectionTitle>
10 Conclusions and Current Directions
</SectionTitle>
    <Paragraph position="0"> In this paper we addressed the problem of automatic topic segmentation of spontaneous dialogue.</Paragraph>
    <Paragraph position="1"> We demonstrated with an empirical evaluation that state-of-the-art approaches fail on spontaneous dialogue because term distribution alone fails to provide adequate evidence of topic shifts in dialogue.</Paragraph>
    <Paragraph position="2"> We have presented a supervised learning algorithm for topic segmentation of dialogue called Museli that combines linguistic features signaling a contribution's function with local context indicators. Our evaluation on two distinct corpora shows a significant improvement over the state-of-the-art algorithms. We have also demonstrated that a significant improvement in performance of state-of-the-art approaches to topic segmentation can be achieved when dialogue exchanges, rather than contributions, are used as the basic unit of discourse. We demonstrated promising results in automatically identifying exchange boundaries.</Paragraph>
  </Section>
class="xml-element"></Paper>