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<Paper uid="P95-1046">
  <Title>Knowledge-based Automatic Topic Identification</Title>
  <Section position="5" start_page="309" end_page="309" type="concl">
    <SectionTitle>
4 Conclusion
</SectionTitle>
    <Paragraph position="0"> The system achieves its current performance without using linguistic tools such as a part-of-speech tagger, syntactic parser, pronoun resoultion algorithm, or discourse analyzer. Hence we feel that the concept counting paradigm is a robust method which can serve as a basis upon which to build an automated text summarization system. The current system draws a performance lower bound for future systems.</Paragraph>
    <Paragraph position="1"> 4This threshold and the starting depth are determined by running the system through different parameter setting. We test ratio = 0.95,0.68,0.45,0.25 and depth = 3,6,9,12. Among them, 7~t = 0.68 and ~D~ = 6 give the best result.</Paragraph>
    <Paragraph position="2"> 5The recall (R) and precision (P) for the three variations axe: vax1(R=0.32,P=0.37), vax2(R=0.30,P=0.34), and vax3(R=0.28,P=0.33) when the system picks 8 sentences.</Paragraph>
    <Paragraph position="3"> We have not yet been able to compare the performance of our system against IR and commerically available extraction packages, but since they do not employ concept counting, we feel that our method can make a significant contribution.</Paragraph>
    <Paragraph position="4"> We plan to improve the system's extraction resuits by incgrporating linguistic tools. Our next goal is generating a summary instead of just extracting sentences. Using a part-of-speech tagger and syntatic parser to distinguish different syntatic categories and relations among concepts; we can find appropriate concept types on the interesting wavefront, and compose them into summary. For example, if a noun concept is selected, we can find its accompanying verb; if verb is selected, we find its subject noun. For a set of selected concepts, we then generalize their matching concepts using the taxonomy and generate the list of {selected concepts + matching generalization} pairs as English sentences.</Paragraph>
    <Paragraph position="5"> There are other possibilities. With a robust working prototype system in hand, we are encouraged to look for new interesting results.</Paragraph>
  </Section>
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