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<Paper uid="P98-2135">
  <Title>Discourse Cues for Broadcast News Segmentation</Title>
  <Section position="7" start_page="821" end_page="821" type="concl">
    <SectionTitle>
6. Conclusion and Future Work
</SectionTitle>
    <Paragraph position="0"> We have described and evaluated a news story segmentation algorithm that detects news discourse structure using discourse cue, s that exploit fixed expressions and transformational-based, part of speech and named entity taggers created using error-driven learning. The implementation utilizes a time-enhanced finite state automata that represents discourse states and their expected temporal occurance in a news broadcast based on statistical analysis of the corpus. This provides an important mechanism to enable topic tracking, indeed we take the text from each segment an run this through a commercial topic identification rouUne an provide the user with a list of the top classes associated with each story (See Figure 3).</Paragraph>
    <Paragraph position="1"> The segmentor has been integrated into a system (BNN) for content-based news access and has been deployed in a corporate intranet and is currently being evaluated for deployment in the US government and a national broadcasting corporation.</Paragraph>
    <Paragraph position="2"> We have improved segmentation performance by exploiting cues in audio and visual streams (e.g., speaker shifts, scene changes) (Maybury et al.</Paragraph>
    <Paragraph position="3"> 1997). To obtain a better indication of annotator reliability and for comparative evaluation, we need to measure interannotator agreement. Future research includes investigating the relationship of other linguistic properties, such as co-reference, intonation contours, and lexical semantics coherence to serve as a measure of cohesion that might further support story segmentation. Finally, we are currently evaluating in user studies which mix of media elements (e.g., key frame, named entities, key sentence) are most effective in presenting story segments for different information seeking tasks (e.g., story identification, comprehension, correlation).</Paragraph>
  </Section>
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