论文标题
通过深度学习技术的多文章摘要：调查
Multi-document Summarization via Deep Learning Techniques: A Survey
论文作者
论文摘要
多文件摘要（MDS）是信息汇总的有效工具，可从与主题相关的文档群中产生信息和简洁的摘要。我们的调查是系统的第一个调查，概述了最近基于深度学习的MDS模型。我们提出了一种新颖的分类法，以总结神经网络的设计策略，并对最新的制作进行全面摘要。我们强调了现有文献中很少讨论的各种目标函数之间的差异。最后，我们提出了与这个新的令人兴奋的领域有关的几个未来方向。
Multi-document summarization (MDS) is an effective tool for information aggregation that generates an informative and concise summary from a cluster of topic-related documents. Our survey, the first of its kind, systematically overviews the recent deep learning based MDS models. We propose a novel taxonomy to summarize the design strategies of neural networks and conduct a comprehensive summary of the state-of-the-art. We highlight the differences between various objective functions that are rarely discussed in the existing literature. Finally, we propose several future directions pertaining to this new and exciting field.
