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<Paper uid="P98-2135">
  <Title>Discourse Cues for Broadcast News Segmentation</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1. Introduction
</SectionTitle>
    <Paragraph position="0"> Large video collections require content-based information browsing, retrieval, extraction, and summarization to ensure their value for tasks such as real-time profiling and retrospective search.</Paragraph>
    <Paragraph position="1"> Whereas image processing for video indexing currently provides low level indec~s such as visual transitions and shot classification (Zhang et al.</Paragraph>
    <Paragraph position="2"> 1994), some research has investigated the use of linguistic streams (e.g., closed captions, transcripts) to provide keyword-based indexes to video. Story-based segmentation remains illusive. For example, traditional text tiling approaches often undersegment broadcast news because of rapid topic shifts (Mani et al. 1997). This paper takes a corpus-based approach to this problem, building linguistic models based on an analysis of a digital collection of broadcast news, exploiting the regularity utilized by humans in signaling topic shifts to detect story segments.</Paragraph>
  </Section>
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