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<Paper uid="W06-1417">
  <Title>Sydney, July 2006. c(c)2006 Association for Computational Linguistics Generation of Biomedical Arguments for Lay Readers</Title>
  <Section position="10" start_page="118" end_page="119" type="relat">
    <SectionTitle>
8 Related Work
</SectionTitle>
    <Paragraph position="0"> Due to space limitations, this section focuses on research on generation of normative arguments (as opposed to behavior-change and evaluative arguments), and arguments designed for text rather than dialogue. Zukerman et al. have presented several papers on argument generation from Bayesian network domain models (e.g., 2000). The type of domain model used in our work differs in two respects. First, it is based on empirical research since it is intended to represent the simplified conceptual model presented to a lay audience in this genre.</Paragraph>
    <Paragraph position="1"> Second, it uses qualitative probabilistic constraints.</Paragraph>
    <Paragraph position="2"> One difference in argument generation is that our system's argument strategies are based on analysis of the corpus. Also, our system creates an intentional-level representation of an argument.</Paragraph>
    <Paragraph position="3"> Teufel and Moens (2002) present a coding scheme for scientific argumentation in research articles that is designed for automatic summarization of human-authored text. Thus, it would not be sufficient for generation from a non-linguistic knowledge base. Also, it does not make the finer-grained distinctions of the Toulmin model.</Paragraph>
    <Paragraph position="4"> Branting et al. (1999) present the architecture of a legal document drafting system. In it, a discourse grammar applies genre-specific knowledge, while a legal reasoning module creates the illocutionary structure of legal arguments. Branting et al. argue for maintaining a distinct intentional-level representation of arguments to support interactive follow-up discussion. We agree, but our design further distinguishes domain reasoning from argument generation.</Paragraph>
    <Paragraph position="5"> As for work on ordering and explicitness, Reed and Long (1997) propose ordering heuristics for  arguments of classical deductive logic. Fiedler and Horacek (2001) present a model for deciding what can be omitted from explanations of mathematical proofs. Carenini and Moore (2000) present an experiment to determine how much evidence is optimal in an evaluative argument.</Paragraph>
  </Section>
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