Opinion summarization with integer linear programming formulation for sentence extraction and ordering

  • Authors:

  • Hitoshi Nishikawa;Takaaki Hasegawa;Yoshihiro Matsuo;Genichiro Kikui

  • Affiliations:

  • NTT Corporation;NTT Corporation;NTT Corporation;NTT Corporation

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

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Abstract

In this paper we propose a novel algorithm for opinion summarization that takes account of content and coherence, simultaneously. We consider a summary as a sequence of sentences and directly acquire the optimum sequence from multiple review documents by extracting and ordering the sentences. We achieve this with a novel Integer Linear Programming (ILP) formulation. Our proposed formulation is a powerful mixture of the Maximum Coverage Problem and the Traveling Salesman Problem, and is widely applicable to text generation and summarization tasks. We score each candidate sequence according to its content and coherence. Since our research goal is to summarize reviews, the content score is defined by opinions and the coherence score is developed in training against the review document corpus. We evaluate our method using the reviews of commodities and restaurants. Our method outperforms existing opinion summarizers as indicated by its ROUGE score. We also report the results of human readability experiments.