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<Paper uid="P01-1023">
  <Title>Empirically Estimating Order Constraints for Content Planning in Generation</Title>
  <Section position="7" start_page="0" end_page="0" type="concl">
    <SectionTitle>
6 Conclusions
</SectionTitle>
    <Paragraph position="0"> In this paper we presented a technique for extracting order constraints among plan elements that performs satisfactorily without the need of large corpora. Using a conservative set of parameters, we were able to reconstruct a good portion of a carefully hand-crafted planner. Moreover, as discussed in the evaluation, there are several pieces of information in the transcripts which are not present in the current system. From our learned results, we have inferred placement constraints of the new information in relation to the previous plan elements without further interviews with experts. null Furthermore, it seems we have captured order-sensitive information in the patterns and freeorder information is kept in the don't care model. The patterns, and ordering constraints among them, provide a backbone of relatively fixed structure, while don't cares are interspersed among them. This model, being probabilistic in nature, means a great deal of variation, but our generated plans should have variability in the right positions. This is similar to findings of floating positioning of information, together with oportunistic rendering of the data as used in STREAK (Robin and McKeown, 1996).</Paragraph>
    <Section position="1" start_page="0" end_page="0" type="sub_section">
      <SectionTitle>
6.1 Future work
</SectionTitle>
      <Paragraph position="0"> We are planning to use these techniques to revise our current content-planner and incorporate information that is learned from the transcripts to increase the possible variation in system output.</Paragraph>
      <Paragraph position="1"> The final step in producing a full-fledged content-planner is to add semantic constraints on the selection of possible orderings. This can be generated through clustering of semantic input to the generator.</Paragraph>
      <Paragraph position="2"> We also are interested in further evaluating the technique in an unrestricted domain such as the Wall Street Journal (WSJ) with shallow semantics such as the WordNet top-category for each NP-head. This kind of experiment may show strengths and limitations of the algorithm in large corpora.</Paragraph>
    </Section>
  </Section>
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