Beyond Opinion Mining: Summarizing Opinions of Customer Reviews

被引:7
作者
Amplayo, Reinald Kim [1 ]
Brazinskas, Arthur [1 ]
Suhara, Yoshi [2 ]
Wang, Xiaolan [3 ]
Liu, Bing [4 ]
机构
[1] Google Res, Mountain View, CA 94043 USA
[2] Grammarly, San Francisco, CA USA
[3] Megagon Labs, Mountain View, CA USA
[4] Univ Illinois, Chicago, IL USA
来源
PROCEEDINGS OF THE 45TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '22) | 2022年
关键词
opinion mining; opinion summarization;
D O I
10.1145/3477495.3532676
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Customer reviews are vital for making purchasing decisions in the Information Age. Such reviews can be automatically summarized to provide the user with an overview of opinions. In this tutorial, we present various aspects of opinion summarization that are useful for researchers and practitioners. First, we will introduce the task and major challenges. Then, we will present existing opinion summarization solutions, both pre-neural and neural. We will discuss how summarizers can be trained in the unsupervised, fewshot, and supervised regimes. Each regime has roots in different machine learning methods, such as auto-encoding, controllable text generation, and variational inference. Finally, we will discuss resources and evaluation methods and conclude with the future directions. This three-hour tutorial will provide a comprehensive overview over major advances in opinion summarization. The listeners will be well-equipped with the knowledge that is both useful for research and practical applications.
引用
收藏
页码:3447 / 3450
页数:4
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