Multi-document Summarization via Deep Learning Techniques: A Survey

• URL: http://arxiv.org/abs/2011.04843v3
• Date: Thu, 9 Dec 2021 02:37:43 GMT
• Title: Multi-document Summarization via Deep Learning Techniques: A Survey
• Authors: Congbo Ma, Wei Emma Zhang, Mingyu Guo, Hu Wang, Quan Z. Sheng
• Abstract summary: We propose a novel taxonomy to summarize the design strategies of neural networks. We highlight the differences between various objective functions that are rarely discussed in the existing literature.
• Score: 29.431160110691607
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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