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<Paper uid="N04-4023">
  <Title>Feature Selection for Trainable Multilingual Broadcast News Segmentation</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> Indexing and retrieving broadcast news stories within a large collection requires automatic detection of story boundaries. This video news story segmentation can use a wide range of audio, language, video, and image features. In this paper, we investigate the correlation between automatically-derived multimodal features and story boundaries in seven different broadcast news sources in three languages. We identify several features that are important for all seven sources analyzed, and we discuss the contributions of other features that are important for a subset of the seven sources.</Paragraph>
  </Section>
class="xml-element"></Paper>