<?xml version="1.0" standalone="yes"?>
<Paper uid="P96-1054">
  <Title>Transitivity and Foregrounding in News Articles: experiments in information retrieval and automatic summarising</Title>
  <Section position="5" start_page="369" end_page="369" type="evalu">
    <SectionTitle>
3 Transitivity and Text Processing
</SectionTitle>
    <Paragraph position="0"> The relationship between transitivity and foregrounding has potential for text processing, in particular, information retrieval and automatic summarising. If it is possible to identify which clauses are central to a text, the information can be used to contribute to a relevance assessment or as the basis for a derived summary.</Paragraph>
    <Section position="1" start_page="369" end_page="369" type="sub_section">
      <SectionTitle>
3.1 Information Retrieval
</SectionTitle>
      <Paragraph position="0"> The standard model of text retrieval is based on the identification of matching query/document terms which are weighted according to their distribution throughout a text database. This model has also been enhanced by a number of linguistic techniques: expansion of query/document terms according to thesaurus relations, synonyms, etc.</Paragraph>
      <Paragraph position="1"> The proposal for this study is to code matching query/document terms for the transitivity value of the clause in which they occur, as a starting point for producing comparative term weights based on linguistic features. Terms which are less central to a discourse will, on this basis, be given lower scores because they occur in low transitivity clauses. The net result will be to produce a document ranking order which more closely represents the importance of the documents to a user. There is also potential for producing a transitivity index for an entire document as well as for individual clauses so that this measure could also feature in a relevance assessment.</Paragraph>
    </Section>
    <Section position="2" start_page="369" end_page="369" type="sub_section">
      <SectionTitle>
3.2 Automatic Summarising
</SectionTitle>
      <Paragraph position="0"> The fundamental task in automatic summarising is to identify the most important sections of a text so that these can be extracted and possibly modified to provide a summary. The notion of transitivity provides a measure against which clauses can be scored.</Paragraph>
      <Paragraph position="1"> The highest scoring clauses, either above a threshold value or on a comparative basis, can then be identified as the basic clauses of a summary. These can either be extracted raw or in context with pronominal references resolved and any logical antecedents included. A previous study in this area (Decker, 1985) extracted clauses and sentences on the basis of syntactic patterns which broadly correlate with certain features of transitivity. The present study focuses on the semantic features of transitivity rather than associated syntax.</Paragraph>
    </Section>
  </Section>
class="xml-element"></Paper>