The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Abstractive Document Summarization with a Graph-Based Attentional Neural Model
Open accessAbstract
Abstractive summarization is the ultimate goal of document summarization research, but previously it is less investigated due to the immaturity of text generation techniques.Recently impressive progress has been made to abstractive sentence summarization using neural models.Unfortunately, attempts on abstractive document summarization are still in a primitive stage, and the evaluation results are worse than extractive methods on benchmark datasets.In this paper, we review the difficulties of neural abstractive document summarization, and propose a novel graph-based attention mechanism in the sequence-to-sequence framework.The intuition is to address the saliency factor of summarization, which has been overlooked by prior works.Experimental results demonstrate our model is able to achieve considerable improvement over previous neural abstractive models.The data-driven neural abstractive method is also competitive with state-of-the-art extractive methods.
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Cites
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A Neural Attention Model for Abstractive Sentence Summarization
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Cited by
Narayan, S., Cohen, S. B., & Lapata, M. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1206 · Cited by 1,207
Neural Text Summarization: A Critical Evaluation
Kryscinski, W., Keskar, N. S., McCann, B., Xiong, C., & Socher, R. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1051 · Cited by 335
Deep Reinforcement Learning for Sequence-to-Sequence Models
Keneshloo, Y., Shi, T., Ramakrishnan, N., & Reddy, C. K. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2929141 · Cited by 230
BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization
Sharma, E., Li, C., & Wang, L. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1212 · Cited by 173
Abstractive Summarization: A Survey of the State of the Art
Lin, H., & Ng, V. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33019815 · Cited by 120
Metadata
- Authors
- Tan, J., Wan, X., & Xiao, J.
- Venue
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2017
- Pages
- 1171-1181
- OpenAlex
- W2741375528
- Cited by
- 325
- References
- 34
- Type
- conference abstract
- Language
- en
- Open access
- hybrid
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