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Guided Structure-Aware Review Summarization

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Abstract

Although the goal of traditional text summarization is to generate summaries with diverse information, most of those applications have no explicit definition of the information structure. Thus, it is difficult to generate truly structure-aware summaries because the information structure to guide summarization is unclear. In this paper, we present a novel framework to generate guided summaries for product reviews. The guided summary has an explicitly defined structure which comes from the important aspects of products. The proposed framework attempts to maximize expected aspect satisfaction during summary generation. The importance of an aspect to a generated summary is modeled using Labeled Latent Dirichlet Allocation. Empirical experimental results on consumer reviews of cars show the effectiveness of our method.

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Authors and Affiliations

  1. State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China

    Feng Jin, Min-Lie Huang & Xiao-Yan Zhu

Authors
  1. Feng Jin
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  2. Min-Lie Huang
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  3. Xiao-Yan Zhu
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Corresponding author

Correspondence to Feng Jin.

Additional information

This work was partly supported by the National Natural Science Foundation of China under Grant Nos. 60973104 and 60803075, and with the aid of a grant from the International Development Research Center, Ottawa, Canada IRCI Project.

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Jin, F., Huang, ML. & Zhu, XY. Guided Structure-Aware Review Summarization. J. Comput. Sci. Technol. 26, 676–684 (2011). https://doi.org/10.1007/s11390-011-1167-y

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