  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.
