Beyond Opinion Mining: Summarizing Opinions of Customer Reviews
- URL: http://arxiv.org/abs/2206.01543v1
- Date: Fri, 3 Jun 2022 12:43:40 GMT
- Title: Beyond Opinion Mining: Summarizing Opinions of Customer Reviews
- Authors: Reinald Kim Amplayo, Arthur Bra\v{z}inskas, Yoshi Suhara, Xiaolan
Wang, Bing Liu
- Abstract summary: 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.
- Score: 20.534293365703427
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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, few-shot, 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.
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