Training a selection function for extraction

  • Authors:
  • Chin-Yew Lin

  • Affiliations:
  • USC/Information Sciences Institute, 4676 Admiralty Way, Marina del Rey, CA

  • Venue:
  • Proceedings of the eighth international conference on Information and knowledge management
  • Year:
  • 1999

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Abstract

In this paper we compare performance of several heuristics in generating informative generic/query-oriented extracts for newspaper articles in order to learn how topic prominence affects the performance of each heuristic. We study how different query types can affect the performance of each heuristic and discuss the possibility of using machine learning algorithms to automatically learn good combination functions to combine several heuristics. We also briefly describe the design, implementation, and performance of a multilingual text summarization system SUMMARIST.