A comparative study on ranking and selection strategies for multi-document summarization

  • Authors:
  • Feng Jin;Minlie Huang;Xiaoyan Zhu

  • Affiliations:
  • Tsinghua University;Tsinghua University;Tsinghua University

  • Venue:
  • COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
  • Year:
  • 2010

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Abstract

This paper presents a comparative study on two key problems existing in extractive summarization: the ranking problem and the selection problem. To this end, we presented a systematic study of comparing different learning-to-rank algorithms and comparing different selection strategies. This is the first work of providing systematic analysis on these problems. Experimental results on two benchmark datasets demonstrate three findings: (1) pairwise and listwise learning-to-rank algorithms outperform the baselines significantly; (2) there is no significant difference among the learning-to-rank algorithms; and (3) the integer linear programming selection strategy generally outperformed Maximum Marginal Relevance and Diversity Penalty strategies.