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.
翻译：在“信息时代”中,客户审查对于做出采购决定至关重要。这种审查可以自动总结,为用户提供意见概览。在这个辅导中,我们介绍对研究人员和从业者有用的意见总结的各个方面。首先,我们将介绍任务和重大挑战。然后,我们将介绍现有的意见总结解决方案,包括前天和神经。我们将讨论如何在不受监督、少见和受监督的制度下对总结者进行培训。每个制度都源于不同的机器学习方法,如自动编码、可控文本生成和变异推论。最后,我们将讨论资源和评估方法,并结束未来的方向。这三小时的辅导将全面概述意见总结的主要进展。听众将精密地掌握对研究和实用应用都有用的知识。
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