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<Paper uid="W06-3309">
  <Title>Generative Content Models for Structural Analysis of Medical Abstracts</Title>
  <Section position="6" start_page="70" end_page="70" type="evalu">
    <SectionTitle>
5 Related Work
</SectionTitle>
    <Paragraph position="0"> Although not the first to employ a generative approach to directly model content, the seminal work of Barzilay and Lee (2004) is a noteworthy point of reference and comparison. However, our study differs in several important respects. Barzilay and Lee employed an unsupervised approach to building topic sequence models for the newswire text genre using clustering techniques. In contrast, because the discourse structure of medical abstracts is well-defined and training data is relatively easy to obtain, we were able to apply a supervised approach.</Paragraph>
    <Paragraph position="1"> Whereas Barzilay and Lee evaluated their work in the context of document summarization, the four-part structure of medical abstracts allows us to conduct meaningful intrinsic evaluations and focus on the sentence classification task. Nevertheless, their work bolsters our claims regarding the usefulness of generative models in extrinsic tasks, which we do not describe here.</Paragraph>
    <Paragraph position="2"> Although this study falls under the general topic of discourse modeling, our work differs from previous attempts to characterize text in terms of domain-independent rhetorical elements (McKeown, 1985; Marcu and Echihabi, 2002). Our task is closer to the work of Teufel and Moens (2000), who looked at the problem of intellectual attribution in scientific texts.</Paragraph>
  </Section>
class="xml-element"></Paper>