The supervised training of high-capacity models on large datasets containing
hundreds of thousands of document-summary pairs is critical to the recent
success of deep learning techniques for abstractive summarization.
Unfortunately, in most domains (other than news) such training data is not
available and cannot be easily sourced. In this paper we enable the use of
supervised learning for the setting where there are only documents available
(e.g.,~product or business reviews) without ground truth summaries. We create a
synthetic dataset from a corpus of user reviews by sampling a review,
pretending it is a summary, and generating noisy versions thereof which we
treat as pseudo-review input. We introduce several linguistically motivated
noise generation functions and a summarization model which learns to denoise
the input and generate the original review. At test time, the model accepts
genuine reviews and generates a summary containing salient opinions, treating
those that do not reach consensus as noise. Extensive automatic and human
evaluation shows that our model brings substantial improvements over both
abstractive and extractive baselines.

本文介绍了一种基于🈚️ground truth summaries 的情况下，利用文档创建合成数据集、引入多种噪声生成函数以及学习生成原始评论的摘要模型的方法，该方法比抽取式和生成式基线模型都有更好的效果。