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<Paper uid="A97-1042">
  <Title>Identifying Topics by Position</Title>
  <Section position="5" start_page="289" end_page="290" type="concl">
    <SectionTitle>
5 Conclusion
</SectionTitle>
    <Paragraph position="0"> This study provides empirical validation for the Position Hypothesis. It also describes a method of deriving an Optimal Position Policy for a collection of texts within a genre, as long as a small set of topic keywords is defined with each text. The Precision and Recall scores indicate the selective power of the Position method on individual topics, while the Coverage scores indicate a kind of upper bound on topics and related material as contained in sentences from human-produced abstracts.</Paragraph>
    <Paragraph position="1"> The results displayed in Figure 13 are especially promising. It is clear that only about 30% of topic keywords are not mentioned in the text directly.</Paragraph>
    <Paragraph position="2"> This is excellent news: it means that as an upper bound, only about 30% of the humans' abstracts in this domain derive from some inference processes, which means that in a computational implementation only about the same amount has to be derived by processes yet to be determined. Second, the title contains about 50% of the topic keywords; the title plus the two most rewarding sentences provide about 60%, and the next five or so add another 6%.</Paragraph>
    <Paragraph position="3"> Thus, a fairly small number of sentences provides 2/3 of the keyword topics.</Paragraph>
    <Paragraph position="4"> It must be remembered that our evaluations treat the abstract as ideal--they rest on the assumption that the central topic(s) of a text are contained in the abstract made of it. In many cases, this is a good assumption; it provides what one may call the author's perspective of the text. But this assumption does not support goal-oriented topic search, in which one wants to know whether a text pertains to some particular prespecified topics. For a goal-oriented perspective, one has to develop a different method to derive an OPP; this remains the topic of  future work.</Paragraph>
    <Paragraph position="5"> Ultimately, the Position Method can only take one a certain distance. Because of its limited power of resolution--the sentence--and its limited method of identification--ordinal positions in a text--it has to be augmented by additional, more precise techniques. But the results gained from what is after all a fairly simple technique are rather astounding nonetheless.</Paragraph>
  </Section>
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