An entity-guided text summarization framework with relational heterogeneous graph neural network
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-023-09247-9.pdf
Reference63 articles.
1. Zhuge H (2016) Multi-dimensional summarization in cyber-physical society. Morgan Kaufmann
2. Wang D, Liu P, Zheng Y, et al. (2020) Heterogeneous graph neural networks for extractive document summarization. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 6209–6219.
3. Sharma E, Huang L, Hu Z, et al. (2019) An entity-driven framework for abstractive summarization. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 3271–3282.
4. Cheng J, Lapata M (2016) Neural summarization by extracting sentences and words. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 484–494.
5. Nallapati R, Zhai F, Zhou B (2017) Summarunner: a recurrent neural network based sequence model for extractive summarization of documents. In: Proceedings of the AAAI Conference on Artificial Intelligence, 3075–3081.
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