The DOI matches its Crossref record; 75 of the 80 references it cites were found in a registry or an index.
Abstractive Summarization: A Survey of the State of the Art
Open accessAbstract
The focus of automatic text summarization research has exhibited a gradual shift from extractive methods to abstractive methods in recent years, owing in part to advances in neural methods. Originally developed for machine translation, neural methods provide a viable framework for obtaining an abstract representation of the meaning of an input text and generating informative, fluent, and human-like summaries. This paper surveys existing approaches to abstractive summarization, focusing on the recently developed neural approaches.
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“relevant topics and techniques have already been studied for at least more than two decades in both IR and NLP communities, e.g., extractive and abstractive summarization that generates summary based on retrieved sentences [87, 88] or answer extraction from top retrieved document [89].”
From Foundations of GenIR (2025) · cites this paper as [88]
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533
BLEU: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2001) · Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02 · doi:10.3115/1073083.1073135 · Cited by 21,344
Sequence to Sequence Learning with Neural Networks
Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766
Neural Machine Translation by Jointly Learning to Align and Translate
Bahdanau, D., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1409.0473 · Cited by 16,213
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1412.3555 · Cited by 11,008
(2004) · Cited by 9,718
Research works printed but not linked: 1
- Ulrich, J.; Murray, G.; and Carenini, G. 2008. A publicly available annotated corpus for supervised email summarization. AAAI EMAIL Workshop .
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Automatic text summarization: A comprehensive survey
El-Kassas, W. S., Salama, C. R., Rafea, A., & Mohamed, H. K. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113679 · Cited by 906
A Survey of Knowledge-enhanced Text Generation
Yu, W., Zhu, C., Li, Z., Hu, Z., Wang, Q., Ji, H., & Jiang, M. (2022) · ACM Computing Surveys · doi:10.1145/3512467 · Cited by 257
A Survey of the State-of-the-Art Models in Neural Abstractive Text Summarization
Syed, A. A., Gaol, F. L., & Matsuo, T. (2021) · IEEE Access · doi:10.1109/access.2021.3052783 · Cited by 112
Abstractive text summarization: State of the art, challenges, and improvements
Shakil, H., Farooq, A., & Kalita, J. K. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128255 · Cited by 100
A Comprehensive Survey of Abstractive Text Summarization Based on Deep Learning
Zhang, M., Zhou, G., Yu, W., Huang, N., & Liu, W. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/7132226 · Cited by 75
Metadata
- Authors
- Lin, H., & Ng, V.
- Affiliations
- The University of Texas at Dallas (US)
- ORCID
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2019-07-17
- Volume
- 33
- Issue
- 01
- Pages
- 9815-9822
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2964813902
- Cited by
- 120
- References
- 80
- Citations by year
- 2026: 3 · 2025: 12 · 2024: 23 · 2023: 26 · 2022: 19 · 2021: 21 · 2020: 16
- Type
- conference abstract
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- www.aaai.org/ (unspecified)
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- Funders
- National Science Foundation (1528037)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- abstractive summarization, text summarization, neural summarization, extractive summarization, machine translation, summary generation
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