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.
@article{arxiv.2011.04843,
title = {Multi-document Summarization via Deep Learning Techniques: A Survey},
author = {Congbo Ma and Wei Emma Zhang and Mingyu Guo and Hu Wang and Quan Z. Sheng},
journal= {arXiv preprint arXiv:2011.04843},
year = {2021}
}