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<Paper uid="I05-2004">
  <Title>A Language Independent Algorithm for Single and Multiple Document Summarization</Title>
  <Section position="6" start_page="22" end_page="23" type="evalu">
    <SectionTitle>
4.5 Related Work
</SectionTitle>
    <Paragraph position="0"> Extractive summarization is considered an important first step for more sophisticated automatic text summarization. As a consequence, there is a large body of work on algorithms for extractive summarization undertaken as part of the DUC evaluation exercises (http://www-nlpir.nist.gov/projects/duc/).</Paragraph>
    <Paragraph position="1"> Previous approaches include supervised learning (Hirao et al., 2002), (Teufel and Moens, 1997), vectorial similarity computed between an initial abstract and sentences in the given document, intra-document similarities (Salton et al., 1997), or graph algorithms (Mihalcea and Tarau, 2004), (Erkan and Radev, 2004), (Wolf and Gibson, 2004). It is also notable the study reported in (Lin and Hovy, 2003b) discussing the usefulness and limitations of automatic sentence extraction for text summarization,  which emphasizes the need of accurate tools for sentence extraction as an integral part of automatic summarization systems.</Paragraph>
  </Section>
class="xml-element"></Paper>