Multi-document Summarization via Deep Learning Techniques: A Survey
By Congbo Ma, W. Zhang, Mingyu Guo, Hu Wang, Quan Z. Sheng. 2020
162 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Jan Leike
- Jeff Wu
- Ryan J. Lowe
- Daniel M. Ziegler
- Paul Christiano
- Long Ouyang
- Nissan Stiennon
- Sergey Levine
In Natural Language Processing
- Paul Christiano
- Daniel M. Ziegler
- Ryan J. Lowe
- Nissan Stiennon
- Jan Leike
- Long Ouyang
- Jeff Wu
- Samuel R. Bowman
Citations in the corpus
- Recursively Summarizing Books with Human Feedback (2021, background): “…2019; Cohan et al., 2018; Xiao and Carenini, 2019; Zhao et al., 2020; Sotudeh et al., 2020), and patents (Sharma et al., 2019), as well as multi-document summarization (Liu et al., 2018; Ma et al., 2020; Gharebagh et al., 2020; Chandrasekaran et al., 2020; Liu and Lapata, 2019a; Gao et al., 2020).”