Computer Science ›› 2020, Vol. 47 ›› Issue (2): 195-200.doi: 10.11896/jsjkx.181202410

• Artificial Intelligence • Previous Articles     Next Articles

Product Review Summarization Using Discourse Hierarchical Structure

ZHANG Yi-fei,WANG Zhong-qing,WANG Hong-ling   

  1. (School of Computer Science & Technology,Soochow University,Suzhou,Jiangsu 215006,China)
  • Received:2018-12-15 Online:2020-02-15 Published:2020-03-18
  • About author:ZHANG Yi-fei,born in 1995,postgra-duate,is member of China Computer Federation (CCF).Her main research interests include natural language processing and product review summarization;WANG Hong-ling,born in 1975,assistant professor,is member of China Computer Federation (CCF).Her main research interests include natural language processing and text summarization.
  • Supported by:
    This work was supported by the Young Scientists Fund of the National Natural Science Foundation of China (61806137, 61702518) and Natural Science Foundation of the Jiangsu Higher Education Institutions of China (18KJB520043).

Abstract: Product review summarization aims to extract a series of relevant sentences that represent the overall opinions of the product.Analysis of discourse hierarchical structure aims to analyze the hierarchical structure and semantic relationship between the various semantic units in the discourse.Obviously,the analysis of discourse hierarchical structure is conducive to determine the semantic information and importance of each semantic unit in the discourse,which is very useful for extracting the important content of the discourse.Therefore,this paper proposed a product review summarization method based on discourse hierarchical structure.This method builds a product review summarization model based on LSTM and applies attention mechanism to extract the important content in the product review by integrating discourse hierarchical structure into the model.The experiments was conducted on the Yelp 2013 dataset and evaluated on the ROUGE evaluation index.The experimental results show that the ROUGE-1 value of the model after adding the discourse hierarchical structure is 0.3608,which is 1.57% higher than the stan-dard LSTM method using only sentences information of the product review.This shows that the introduction of discourse hierarchical structure into the product review summarization task can effectively improve the performance of the task.

Key words: Attention mechanism, Discourse hierarchical structure, LSTM, Neural network, Product review summarization

CLC Number: 

  • TP391
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