该文研究面向在线顾客点评的面向属性抽取式观点摘要问题。传统方法主要考虑如何抽取属性相关观点,该文提出进一步考虑观点的富含信息(informativeness)、重要性(salience)及多样性 (diversity)这三方面要求。该文提出了一个基于带汇点的流形排序的一体化的摘要抽取模型,在一体化的流形排序过程中同时考虑三方面要求。 在餐馆点评数据上的实验表明了所提出三方面要求的合理性及摘要抽取模型的有效性。
Abstract
In this poster, we consider the problem of aspect-based extractive opinion summarization of online reviews. In additior to extracting aspect-relevant opinions as most existing approach do, we propose to further consider the requirements of informativeness, salience, and diversity in order to generate a high-quality summary. We proposed a unified summary extracting framework based on manifold ranking with sink points to address the three proposed requirements in a unified ranking process. Experiments with restaurant reviews show the reason-ability of the proposed requirements and effectiveness of the proposed approach.
关键词
在线顾客点评 /
面向属性抽取式观点摘要 /
带汇点的流形排序 /
属性观点联合模型。
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Key words
online reviews /
aspect-based extractive opinion summarization /
manifold ranking with sink points /
Joint Aspect/Opinion model
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参考文献
[1] K Lerman, S Blair-Goldensohn, R McDonald. Sentiment summarization: evaluating and learning user preferences[C]//Proceedings of the EACL 09, 2009:514-522.
[2] M Hu, B Liu. Mining and summarizing customer reviews[C]//Proceedings of the SIGKDD, 2004:168-177.
[3] S Blair-Goldensohn, K Hannan, R McDonald, et al. Building a sentiment summarizer for local service reviews[C]//Proceeding of the WWW Workshop on NLP in the Information Explosion Era, 2008.
[4] X Ling, Q Mei, C Zhai, et al. Mining multi-faceted overviews of arbitrary topics in a text collection[C]//Proceeding of the 14th ACM SIGKDD, 2008: 497-505.
[5] X Zhao, J Jiang, H Yan, et al. Jointly modeling aspects and opinions with a MaxEnt-LDA hybrid[C]//Proceeding of the EMNLP 2010, 2010: 56-65.
[6] X Cheng, P Du, J Guo, et al. Ranking on data manifold with sink points [J]. IEEE Transactions on Knowledge and Data Engineering, 2013,25(1): 177-191.
[7] I Titov, R McDonald. A joint model of text and aspect ratings for sentiment summarization[C]//Proceedings of the ACL-08:HLT,2008.
[8] D Blei, A Ng, M Jordan. Latent Dirichlet allocation[J]. Journal of Machine Learning Research, 2003, 3(3): 993-1022.
[9] T Griffiths, M Steyvers. Finding scientific topics[C]//Proceedings of the National Academy of Sciences, 101(Suppl 1), 2004: 5228-5535.
[10] D Zhou, J Weston, A Gretton, et al. Ranking on data manifolds[C]//Proceedings of Advances in Neural Information Processing System 16, 2004.
[11] G Ganu, N Elhadad, A Marian. Beyond the stars: improving rating predictions using review text content[C]//Proceedings of International Workshop on the Web and Databases, 2009.
[12] S Brody, N Elhadad. An unsupervised aspect-sentiment model for online reviews[C]//Proceedings of Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics (HLT 10), 2010.
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脚注
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基金
国家自然科学基金(61232010、60933005、60903139、61202215及61100083); 国家242信息安全计划课题(2011F65); 国家信息安全测评中心项目(Z1277)。
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