Automatic text summarization of scientific articles based on classification of extract's population

  • Authors:

  • Maher Jaoua;Abdelmajid Ben Hamadou

  • Affiliations:

  • Faculté des Sciences Economiques et de Gestion de Sfax, Laboratoire LARIS, Sfax, Tunisie;Faculté des Sciences Economiques et de Gestion de Sfax, Laboratoire LARIS, Sfax, Tunisie

  • Venue:
  • CICLing'03 Proceedings of the 4th international conference on Computational linguistics and intelligent text processing
  • Year:
  • 2003

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Abstract

We propose in this paper a summarization method that creates indicative summaries from scientific papers. Unlike conventional methods that extract important sentences, our method considers the extract as the minimal unit for extraction and uses two steps: the generation and the classification. The first step combines text sentences to produce a population of extracts. The second step evaluates each extract using global criteria in order to select the best one. In this case, the criteria are defined according to the whole extract rather than sentences. We have developed a prototype of the summarization system for French language called ExtraGen that implements a genetic algorithm simulating the mechanism of generation and classification.