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<Paper uid="W00-0404">
  <Title>Extracting Key Paragraph based on Topic and Event Detection -- Towards Multi-Document Summarization</Title>
  <Section position="8" start_page="75" end_page="75" type="concl">
    <SectionTitle>
7 Conclusion
</SectionTitle>
    <Paragraph position="0"> In this paper, we proposed a method for extracting key paragraph for summarization based on distinction between a topic and an event. The results showed that the average accuracy was 68.1~ when we used the TDT1 corpus. TIPSTER Text Summarization Evaluation (SUMMAC) proposed various methods for evaluating document summarization and tasks (Mani et al., 1999). Of these, participants submitted two summaries: a fixed-length summary limited to 10% of tile length of the source, and a summary which was not limited in length. Future work includes quantitative and qualitative evaluation. In addition, our method used single words rather thaaa phrases. These phrases, however, would be helpful to resolve ambiguity and reduce a lot of noise, i.e. yield much better accuracy. We plaal to apply our method to phrase-based topic and event extraction, then turn to focus on the problem that how to form the actual summary..</Paragraph>
  </Section>
class="xml-element"></Paper>