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<Paper uid="W97-0704">
  <Title>Automated Text Summarization in SUMMARIST</Title>
  <Section position="8" start_page="22" end_page="22" type="concl">
    <SectionTitle>
3 Conclusion
</SectionTitle>
    <Paragraph position="0"> As outhned .in. Section I, extract summaries reqmre only the stage of topic identification By including modules to perform topic interpretation and summary generaUon, SUlVlMARIST will also be able to produce abstract summaries How well ~t wdl do so ts a matter for future mvemgatlon An important aspect to be addressed is the combination of the outputs of various modules m each stage We plan to investigate different approaches, from a simple combination by votes to methods for automattcally training relattve strengths of contribution Automated summarmatlon LS sunultaneously an old topic--work on tt dates from the 1950's----and a new toplc--tt ts so difficult that mterestlng headway can be made for many years to come We are excited about the posslbflmes offered by the combination of semantic and statmtlcal techmques m what is, qmte possibly, the most complex task of all NLP</Paragraph>
  </Section>
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