Beyond Opinion Mining: Summarizing Opinions of Customer Reviews

TL;DR

This tutorial provides a comprehensive overview of opinion summarization techniques for customer reviews, covering methods, challenges, training regimes, resources, and future directions to aid research and practical applications.

Contribution

It offers a detailed survey of both pre-neural and neural opinion summarization methods, including training strategies and evaluation approaches, filling a gap in consolidated knowledge.

Findings

Survey of existing opinion summarization solutions

Discussion of training regimes and machine learning methods

Overview of resources and evaluation techniques

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.