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<Paper uid="W97-0715">
  <Title>A Formal Model of Text Summarization Based on Condensation Operators of a Terminological Logic</Title>
  <Section position="5" start_page="101" end_page="101" type="relat">
    <SectionTitle>
4 Related Work
</SectionTitle>
    <Paragraph position="0"> The task dommn of text summarization is characterized by a ~clash of cwshzatwns&amp;quot; From the point of view of natural language understanding proper (Schank &amp; Abelson 77, Dyer 83) it ts considered a heavdy knowledge-based task reqmnng a substantial knowledge background In the field of mformahon retneval, however, the corresponding task of automahc abstracting, has been considered from Its very beganmng (Luhn 58), a problem that can be dealt with by surface-level pattern matching techmques and statLshcal methods originally developed for lexlcal selection tasks such as automahc mdeydng or classlficahon (Salton et al 94) Thin approach has recently been given a lot of attenhon agaan, mmnly due to the renamsance of statlshcal methodology m the field of parsing and tagging (Kuplec 95) Given a stahstlcal approach, however, automahc abstracting bods down to a sentence extrachon problem, vsz deterrrmnmg the most salient sentences based on surface-level lexlcal or positional lndicatom We adhere to the knowledge-based paradigm of abstractmg and propose to fully integrate text knowledge abstraction m a terminological reasonmg model In such an approach, text understanding and summarlzatton are considered within a formally homogeneous framework Moreover, and most important, this model allows for a staged provmon of mformatwn m summaries based on conceptual criteria (as illustrated by the chscusslon of text graphs) Such a funchonallty is unhkely to be achieved by surface-oriented approaches due to their inherent hmltahons to provide cohesive summaries from large sets of extracted sentences (Pmce 90)</Paragraph>
  </Section>
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