导航菜单

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).

PDF (PC)

137

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

CLC Number: 

  • TP391
[1]KANG M,AHN J,LEE K,et al.Opinion mining using ensemble text hidden Markov models for text classification[J].Expert Systems With Applications,2017,94.
[2]LY D K,SUGIYAMA K,LIN Z,et al.Product review summarization from a deeper perspective[C]∥ACM IEEE Joint Conference On Digital Libraries.ACM,2011:311-314.
[3]YATANI K,NOVATI M,TRUSTY A,et al.Analysis of adjective-noun word pair extraction methods for online review summarization[C]∥International Joint Conference on Artificial Intelligence.AAAI Press,2011:2771-2776.
[4]ALLAHYARI M,POURIYEH S A,ASSEFI M,et al.Text Summarization Techniques:A Brief Survey[J].International Journal of Advanced Computer Science and Applications,2017,8(10):397-405.
[5]HOCHREITER S,SCHMIDHUBER J.Long short-term memory[J].Neural Computation,1997,9(8):1735-1780.
[6]SUN Q Y,WANG Z Q,ZHU Q M,et al.Stance Detection with Herarchical Attention Network[C]∥Proceedings of the 27th International Conference on Computational Linguistics.Santa Fe,New Mexico,USA,2018:20-26.
[7]HU M,LIU B.Mining opinion features in customer reviews.[C]∥National Conference on Artificial Intelligence,American Association for Artificial Intelligence.2004:755-760.
[8]HU M,LIU B.Mining and summarizing customer reviews[C]∥Knowledge Discovery and Data Mining.ACM,2004:168-177.
[9]NISHIKAWA H,HASEGAWA T,MATSUO Y,et al.Optimizing Informativeness and Readability for Sentiment Summarization[C]∥Meeting of the Association for Computational Linguistics.2010:325-330.
[10]GANESAN K,ZHAI C,HAN J,et al.Opinosis:A Graph Based Approach to Abstractive Summarization of Highly Redundant Opinions[C]∥International Conference on Computational Linguistics.2010:340-348.
[11]GANESAN K,ZHAI C,VIEGAS E,et al.Micropinion generation:an unsupervised approach to generating ultra-concise summaries of opinions[C]∥International World Wide Web Conferences.ACM,2012:869-878.
[12]GERANI S,MEHDAD Y,CARENINI G,et al.Abstractive Summarization of Product Reviews Using Discourse Structure[C]∥Empirical Methods in Natural Language Processing.2014:1602-1613.
[13]LI Y C.Research of Chinese Discourse Structure Representation And Resource Construction[D].Soochow:Soochow University,2015.
[14]SHAN Y M.Formal Analyses of Chinese Text Structure and Its Indexing Algorithm[C]∥China National Conference on Computational Linguistics.Journal of Chinese information processing,2001.
[15]YANG J,HOU M,WANG N,et al.Sentiment Polarity Analysis of Reviews Based on Shallow Text Structure[J].Journal of Chinese Information Processing,2011,25(2):83-89.
[16]TANG D,QIN B,LIU T,et al.Document Modeling with Gated Recurrent Neural Network for Sentiment Classification[C]∥Empirical Methods in Natural Language Processing.Association for Computational Linguistics,2015:1422-1432.
[17]YANG Z,YANG D,DYER C,et al.Hierarchical Attention Networks for Document Classification[C]∥North American Chapter of the Association for Computational Linguistics.Association for Computational Linguistics,2016:1480-1489.
[18]REN P,CHEN Z,REN Z,et al.Leveraging Contextual Sentence Relations for Extractive Summarization Using a Neural Attention Model[C]∥International ACM SIGIR Conference on Research and Development in Information Retrieval.ACM,2017:95-104.
[19]SINHA A,YADAV A,GAHLOT,et al.Extractive Text Summarization using Neural Networks[J].arXiv:Computation and Language,2018.
[20]LIN C Y,HOVAY E.Automatic evaluation of summaries using N-gram co-occurrence statistics[C]∥Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology.2003:71-78.
[21]WAN X,YANG J.Multi-document summarization using cluster-based link analysis[C]∥Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.ACM,2008:299-306.
[22]PEI Y,YIN W,FAN Q,et al.A Supervised Aggregation Framework for Multi-Document Summarization[C]∥International Conference on Computational Linguistics.2012:2225-2242.
[1] XU Mao,HOU Jin,WU Pei-jun,LIU Yu-ling,LV Zhi-liang. Convolutional Neural Networks Based on Time-Frequency Characteristics for Modulation Classification [J]. Computer Science, 2020, 47(2): 175-179.
[2] WANG Li-hua,DU Ming-hui,LIANG Ya-ling. Classification Net Based on Angular Feature [J]. Computer Science, 2020, 47(2): 83-87.
[3] LIU Xiao-tong,WANG Wei,LI Ze-yu,SHEN Si-wan,JIANG Xiao-ming. Recognition Algorithm of Red and White Cells in Urine Based on Improved BP Neural Network [J]. Computer Science, 2020, 47(2): 102-105.
[4] FU Xue-yang,SUN Qi,HUANG Yue,DING Xing-hao. Single Image De-raining Method Based on Deep Adjacently Connected Networks [J]. Computer Science, 2020, 47(2): 106-111.
[5] QIAN Xiao-mei,LIU Jia-yong,CHENG Peng-sen. Distant Supervised Relation Extraction Based on Densely Connected Convolutional Networks [J]. Computer Science, 2020, 47(2): 157-162.
[6] XU Ming,JIAO Jian-jun,LONG Wen. Sine Cosine Algorithm Based on Logistic Model and Stochastic Differential Mutation [J]. Computer Science, 2020, 47(2): 206-212.
[7] CUI Yan-peng,LIU Mi,HU Jian-wei. Malicious Web Request Detection Technology Based on CNN [J]. Computer Science, 2020, 47(2): 281-286.
[8] HU Zhi-jun,XU Yong. Overview of Content-based Video Retrieval [J]. Computer Science, 2020, 47(1): 117-123.
[9] XU Fei-xiang,YE Xia,LI Lin-lin,CAO Jun-bo,WANG Xin. Comprehensive Calculation of Semantic Similarity of Ontology Concept Based on SA-BP Algorithm [J]. Computer Science, 2020, 47(1): 199-204.
[10] SUN Ming-xuan,WENG Ding-en,ZHANG Yu. Time-variant Neurocomputing with Finite-value Terminal Recurrent Neural Networks [J]. Computer Science, 2020, 47(1): 212-218.
[11] LI Yuan,LI Zhi-xing,TENG Lei,WANG Hua-ming,WANG Guo-yin. Comment Sentiment Analysis and Sentiment Words Detection Based on Attention Mechanism [J]. Computer Science, 2020, 47(1): 186-192.
[12] XU Yang,WANG Jian-cheng,LIU Qi-yuan,LI Shou-shan. Intention Detection in Spoken Language Based on Context Information [J]. Computer Science, 2020, 47(1): 205-211.
[13] YANG Dan-hao,WU Yue-xin,FAN Chun-xiao. Chinese Short Text Keyphrase Extraction Model Based on Attention [J]. Computer Science, 2020, 47(1): 193-198.
[14] LI Qing-hua, LI Cui-ping, ZHANG Jing, CHEN Hong, WANG Shao-qing. Survey of Compressed Deep Neural Network [J]. Computer Science, 2019, 46(9): 1-14.
[15] WANG Xin, MENG Hao-hao, JIANG Xiao-tao, CHEN Sheng-yong, SUN Ling-yun. Survey on Character Motion Synthesis Based on Neural Network [J]. Computer Science, 2019, 46(9): 22-27.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] FAN Ji-li, LI Xiao-hua, NIE Tie-zheng, YU Ge. Survey on Smart Contract Based on Blockchain System[J]. Computer Science, 2019, 46(11): 1 -10 .
[2] WANG Xiao-xia, SUN De-cai. Q-sample-based Local Similarity Join Parallel Algorithm[J]. Computer Science, 2019, 46(12): 38 -44 .
[3] HUANG Meng-ting, ZHANG Ling, JIANG Wen-chao. Short Text Feature Expansion and Classification Based on Non-negative Matrix Factorization[J]. Computer Science, 2019, 46(12): 69 -73 .
[4] XU Shu-yan, HAN Li-xin, XU Guo-xia. Domain Adaptation Algorithm Based on Tensor Decomposition[J]. Computer Science, 2019, 46(12): 89 -94 .
[5] CHEN Zi-hao, LI Qiang. Improved PBFT Consensus Mechanism Based on K-medoids[J]. Computer Science, 2019, 46(12): 101 -107 .
[6] YIN Jia, GUAN Xin-jie, BAI Guang-wei. Task Offloading and Cooperative Load Balancing Mechanism Based on Mobile Edge Computing[J]. Computer Science, 2019, 46(12): 126 -131 .
[7] WANG Hui, ZHOU Ming-ming. Medical Information Security Storage Model Based on Blockchain Technology[J]. Computer Science, 2019, 46(12): 174 -179 .
[8] GAO Dan, LING Jie, CHEN Jia-hui. Two-dimensional Code Encryption Based on Revocable Outsourced Attribute Encryption[J]. Computer Science, 2019, 46(12): 186 -191 .
[9] WANG Yang, LI Peng, JI Yi-mu, FAN Wei-bei, ZHANG Yu-jie, WANG Ru-chuan, CHEN Guo-liang. High Performance Computing and Astronomical Data:A Survey[J]. Computer Science, 2020, 47(1): 1 -6 .
[10] XU Chuan-fu,WANG Xi,LIU Shu,CHEN Shi-zhao,LIN Yu. Large-scale High-performance Lattice Boltzmann Multi-phase Flow Simulations Based on Python[J]. Computer Science, 2020, 47(1): 17 -23 .
渝ICP备16013121号-4
Copyright © 2019 Editorial Office of Computer Science
Add:18# Honghu West Rd., Yubei District,Chongqing,China    Zip Code: 401121
Editorial Office: 023-63500828    E-mail: jsjkx12@163.com    jsjkx_bianwu@163.com(Finance)