A bottom-up approach to sentence ordering for multi-document summarization

  • Authors:

  • Danushka Bollegala;Naoaki Okazaki;Mitsuru Ishizuka

  • Affiliations:

  • The University of Tokyo, Bunkyo-ku, Tokyo, Japan;The University of Tokyo, Bunkyo-ku, Tokyo, Japan;The University of Tokyo, Bunkyo-ku, Tokyo, Japan

  • Venue:
  • ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

Abstract

Ordering information is a difficult but important task for applications generating natural-language text. We present a bottom-up approach to arranging sentences extracted for multi-document summarization. To capture the association and order of two textual segments (eg, sentences), we define four criteria, chronology, topical-closeness, precedence, and succession. These criteria are integrated into a criterion by a supervised learning approach. We repeatedly concatenate two textual segments into one segment based on the criterion until we obtain the overall segment with all sentences arranged. Our experimental results show a significant improvement over existing sentence ordering strategies.