Identifying Topics by Position


Bibliographical data

  • Source: Proceedings of the 5th Conference on Applied Natural Language Processing
  • Pages: 283-290
  • Year: 1997
  • Authors: Chin-Yew Lin and Eduard Hovy
  • Publisher: ACL



This paper addresses the problem of identifying likely topics of texts by their position in the text. It describes the automated training and evaluation of an Optimal Position Policy, a method of locating the likely positions of topic-bearing sentences based on genre-specific regularities of discourse structure. This method can be used in applications such as information retrieval, routing, and text summarization.