Multi-document summarization (MDS) is an effective tool for information aggregation which generates an informative and concise summary from a cluster of topic-related documents. Our survey structurally overviews the recent deep learning based multi-document summarization models via a proposed taxonomy and it is the first of its kind. Particularly, we propose a novel mechanism to summarize the design strategies of neural networks and conduct a comprehensive summary of the state-of-the-art. We highlight the differences among various objective functions which are rarely discussed in the existing literature. Finally, we propose several future directions pertaining to this new and exciting development of the field.

翻译：多文件总结(MDS)是信息汇总的有效工具,它从一组专题相关文件中产生一份内容丰富和简明的概要。我们的调查从结构上概述了最近通过拟议的分类法进行的深入学习的多文件总结模式,这是此类模式中的第一个。我们特别提出了一个新机制,以总结神经网络的设计战略,全面总结最新技术。我们强调现有文献很少讨论的各种目标功能之间的差异。最后,我们提出了与该领域这一新和令人振奋的发展有关的若干未来方向。