Neural Summarization is a computational approach to automatic text summarization that employs deep neural network models to generate concise summaries by learning hierarchical representations of source documents.
664
Publications
57.3K
Citations
1.7K
Authors
543
Institutions
Publications per year
2017–2026
566
Parents
Publications
664
Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond
Ramesh Nallapati, Bowen Zhou, Cícero dos Santos et al. · 2016 · 2.2K citations · Full text
Hierarchical Neural Story Generation
Angela Fan, Mike Lewis, Yann Dauphin · 2018 · 1.1K citations · Full text
1–5 of 664
Authors
1.7K
- ML
14
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- FW
14
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- BZ
11
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- XQ
10
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- PJ
10
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1–5 of 1.7K
Institutions
543
Peking University
Beijing, China
23 h-index
85 publications
6.2K citations
Carnegie Mellon University
Pittsburgh, United States
19 h-index
54 publications
4.1K citations
Google (United States)
Mountain View, United States
16 h-index
71 publications
8.4K citations
University of Edinburgh
Edinburgh, United Kingdom
16 h-index
51 publications
8.6K citations
Chinese Academy of Sciences
Beijing, China
15 h-index
50 publications
1.9K citations
University of Chinese Academy of Sciences
Beijing, China
14 h-index
50 publications
2.8K citations
Microsoft Research (United Kingdom)
Cambridge, United Kingdom
12 h-index
29 publications
1.4K citations
Microsoft Research Asia (China)
Beijing, China
11 h-index
28 publications
4.7K citations
1–8 of 543
Venues
- arXiv (Cornell University)
Repository · 120 publications · 8.1K citations
43
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- Proceedings of the AAAI Conference on Artificial Intelligence
Conference · 21 publications · 2.4K citations
18
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- Expert Systems with Applications
19 publications · 2.1K citations
16
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- IEEE Access
12 publications · 436 citations
12
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- Multimedia Tools and Applications
11 publications · 584 citations
11
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