The identification of important concepts in highly structured technical papers

ABSTRACT

Automatic abstracting, typically based on extraction of important sentences from a text, has been treated as a largely separate task from automatic indexing. This paper describes an approach in which the indexing and abstracting tasks are effectively combined. It is applicable to highly structured empirical research papers, whose content can be organised using a semantic frame. During a scan of a source text, stylistic clues and constructs are used for extracting candidate fillers for the various slots in the frame. Subsequently, an actual concept name is chosen for each slot by comparing the various candidates and their weights.

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.7 Natural Language Processing
          Subjects: Text analysis

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing
          Subjects: Indexing methods

  I. Computing Methodologies
  I.7 DOCUMENT AND TEXT PROCESSING

General Terms:
Algorithms, Design