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<Paper uid="C04-1110">
  <Title>Semantic Similarity Applied to Spoken Dialogue Summarization</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We present a novel approach to spoken dialogue summarization. Our system employs a set of semantic similarity metrics using the noun portion of WordNet as a knowledge source. So far, the noun senses have been disambiguated manually. The algorithm aims to extract utterances carrying the essential content of dialogues. We evaluate the system on 20 Switchboard dialogues. The results show that our system out-performs LEAD, RANDOM and TF*IDF baselines. null</Paragraph>
  </Section>
class="xml-element"></Paper>