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<Paper uid="P04-1049">
  <Title>Paragraph-, word-, and coherence-based approaches to sentence ranking: A comparison of algorithm and human performance</Title>
  <Section position="6" start_page="4" end_page="4" type="concl">
    <SectionTitle>
5 Conclusion
</SectionTitle>
    <Paragraph position="0"> The goal of this paper was to evaluate the results of three different kinds of sentence ranking algorithms and one commercially available summarizer. In order to evaluate the algorithms, we compared their sentence rankings to human sentence rankings of fifteen texts of varying length from the Wall Street Journal.</Paragraph>
    <Paragraph position="1"> Our results indicated that a simple paragraph-based algorithm that was intended as a baseline performed very poorly, and that word-based and some coherence-based algorithms showed the best performance. The only commercially available summarizer that we tested, the MSWord summarizer, showed worse performance than most other algorithms. Furthermore, we found that a coherence-based algorithm that uses PageRank and takes non-tree coherence graphs as input performed better than most versions of a coherence-based algorithm that operates on coherence trees. When data from Experiments 1 and 2 were collapsed, the PageRank algorithm performed significantly better than all other algorithms, except the coherence-based algorithm that uses in-degrees of nodes in non-tree coherence graphs.</Paragraph>
  </Section>
class="xml-element"></Paper>