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<Paper uid="C02-1053">
  <Title>Extracting Important Sentences with Support Vector Machines</Title>
  <Section position="7" start_page="4" end_page="4" type="concl">
    <SectionTitle>
6 Conclusion
</SectionTitle>
    <Paragraph position="0"> This paper presented a SVM-based important sentence extraction technique. Comparisons were made using the lead-based method, decisiontreelearningmethod,andboostingmethod null with the summarization rates of 10%, 30%, and 50%. The experimental results show that the SVM-based method outperforms the other methods at all summarization rates. Moreover, we clarified the effective features for three genres, and showed that the important features vary with the genre.</Paragraph>
    <Paragraph position="1"> Inourfuturework,wewouldliketoapplyour method to trainable Question Answering System SAIQA-II developed in our group.</Paragraph>
  </Section>
class="xml-element"></Paper>