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<Paper uid="C90-2048">
  <Title>THE GENERATION OF HIGH-LEVEL STRUCTURE FOR EXTENDED EXPLANATIONS</Title>
  <Section position="12" start_page="279" end_page="280" type="concl">
    <SectionTitle>
4 CONCLUSIONS
</SectionTitle>
    <Paragraph position="0"> This paper has presented a computational model for generating the high-level structure for extended explanations. It is being implemented as part of DIALS (Delaware Intelligent Advisory Language System), using the NIKL knowledge representation system.</Paragraph>
    <Paragraph position="1"> No claims are made that the methodology represents a psychological model of composition. However, it  is maintained that the text produced by this model is typical of that encountered in many naturally-occurring extended explanations. The model does not presume to generate all acceptable organizations; indeed, there are many welLstruetured explanations for which it does not account. We have presented the simplest ease in which a high-level organization is constructed from a parent and its children in a taxonomic hierarchy. However, many other possibilities exist, several of which are currently being examined within the semantic network paradigm. In addition, we are investigating the generation of appropriate recovery strategies in response to interruptions and their impact on the planning strategy. We are also incorporating several forms of repetition that are widely used in extended explanations.</Paragraph>
    <Paragraph position="2"> The unit of discourse which is the major motivation tbr this model is the basic block, a primary segment of text which occupies the tirst level of the discourse hierarchy. It consists of an organizational focus and text constructed about that focus. Wilile methods such as RST and schemas are adequate for local organization of text, they are insufficient for the determination of high-level structure, providing neither criteria which describe what constitutes an acceptable framework for a discourse, nor any clues as to how this framework should be constructed. This paper provides both. We believe that our model provides strategies essential for bridging the gap between the generation of short explanations and the successful generation of extended explanations.</Paragraph>
  </Section>
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