Enhancing abstractive summarization of implicit datasets with contrastive attention

Author:

Kwon Soonki,Lee YounghoonORCID

Funder

nrf

Publisher

Springer Science and Business Media LLC

Reference42 articles.

1. Rush AM, Chopra S, Weston J (2015) A neural attention model for abstractive sentence summarization. In: Proceedings of the 2015 conference on empirical methods in natural language processing, pp. 379–389

2. Nallapati R, Zhou B, Santos C, Gulçehre Ç, Xiang B (2016) Abstractive text summarization using sequence-to-sequence RNNs and beyond. In: Proceedings of the 20th SIGNLL conference on computational natural language learning, pp. 280–290

3. See A, Liu PJ, Manning CD (2017) Get to the point: summarization with pointer-generator networks. In: Proceedings of the 55th annual meeting of the association for computational linguistics (Volume 1: Long Papers), pp. 1073–1083

4. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. Advances in neural information processing systems. 30

5. Devlin J, Chang M-W, Lee K, Toutanova K (2019) Bert: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, Volume 1 (Long and Short Papers), pp. 4171–4186

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