Automatic text summarization (ATS) has achieved impressive performance thanks to recent advances in deep learning (DL) and the availability of large-scale corpora. The key points in ATS are to estimate the salience of information and to generate coherent results. Recently, a variety of DL-based approaches have been developed for better considering these two aspects. However, there is still a lack of comprehensive literature review for DL-based ATS approaches. The aim of this paper is to comprehensively review significant DL-based approaches that have been proposed in the literature with respect to the notion of generic ATS tasks and provide a walk-through of their evolution. We first give an overview of ATS and DL. The comparisons of the datasets are also given, which are commonly used for model training, validation, and evaluation. Then we summarize single-document summarization approaches. After that, an overview of multi-document summarization approaches is given. We further analyze the performance of the popular ATS models on common datasets. Various popular approaches can be employed for different ATS tasks. Finally, we propose potential research directions in this fast-growing field. We hope this exploration can provide new insights into future research of DL-based ATS.

由于深度学习 (DL) 的最新进展和大规模语料库的可用性，自动文本摘要 (ATS) 取得了令人印象深刻的性能。ATS 中的关键点是估计信息的显着性并生成连贯的结果。最近，为了更好地考虑这两个方面，已经开发了各种基于 DL 的方法。然而，基于 DL 的 ATS 方法仍然缺乏全面的文献综述。本文的目的是全面回顾文献中关于通用 ATS 任务概念的重要基于 DL 的方法，并提供其演变的演练。我们首先概述 ATS 和 DL。还给出了数据集的比较，这些数据集通常用于模型训练、验证和评估。然后我们总结了单文档摘要方法。之后，给出了多文档摘要方法的概述。我们进一步分析了流行的 ATS 模型在常见数据集上的性能。各种流行的方法可以用于不同的 ATS 任务。最后，我们提出了这个快速发展领域的潜在研究方向。我们希望这种探索可以为基于 DL 的 ATS 的未来研究提供新的见解。