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<Paper uid="P99-1078">
  <Title>Using Linguistic Knowledge in Automatic Abstracting</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> The idea of producing abstracts or summaries by automatic means is not new, several methodologies have been proposed and tested for automatic abstracting including among others: word distribution (Luhn, 1958); rhetorical analysis (Marcu, 1997); and probabilistic models (Kupiec et al., 1995). Even though some approaches produce acceptable abstracts for specific tasks, it is generally agreed that the problem of coherent selection and expression of information in automatic abstracting remains (Johnson, 1995). One of the main problems is how to ensure the preservation of the message of the original text if sentences picked up from distant parts of the source text are juxtaposed and presented to the reader.</Paragraph>
    <Paragraph position="1"> Rino and Scott (1996) address the problem of coherent selection for gist preservation, however they depend on the availability of a complex meaning representation which in practice is difficult to obtain from the raw text.</Paragraph>
    <Paragraph position="2"> In our work, we are concerned with the automatic generation of short indicative-informative abstract for technical and scientific papers. We base our methodology on a study of a corpus of professional abstracts and source or parent documents. Our method also considers the reader's interest as essential in the process of abstracting. null</Paragraph>
  </Section>
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