Content selection from an ontology-based knowledge base for the generation of football summaries

  • Authors:
  • Nadjet Bouayad-Agha;Gerard Casamayor;Leo Wanner

  • Affiliations:
  • DTIC, University Pompeu, Fabra Barcelona, Spain;DTIC, University Pompeu, Fabra Barcelona, Spain;ICREA and DTIC, University Pompeu, Fabra Barcelona, Spain

  • Venue:
  • ENLG '11 Proceedings of the 13th European Workshop on Natural Language Generation
  • Year:
  • 2011

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Abstract

We present an approach to content selection that works on an ontology-based knowledge base developed independently from the task at hand, i.e., Natural Language Generation. Prior to content selection, a stage akin to signal analysis and data assessment used in the generation from numerical data is performed for identifying and abstracting patterns and trends, and identifying relations between individuals. This new information is modeled as an extended ontology on top of the domain ontology which is populated via inference rules. Content selection leverages the ontology-based description of the domain and is performed throughout the text planning at increasing levels of granularity. It includes a main topic selection phase that takes into account a simple user model, a set of heuristics, and semantic relations that link individuals of the KB. The heuristics are based on weights determined empirically by supervised learning on a corpus of summaries aligned with data. The generated texts are short football match summaries that take into account the user perspective.