References
[1]. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. In Advances in neural information processing systems (pp. 5998-6008).
[2]. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
[3]. Reimers, N., & Gurevych, I. (2019). Sentence-bert: Sentence embeddings using siamese bert-networks. arXiv preprint arXiv:1908.10084.
[4]. Park, J. W. (2020). Continual bert: Continual learning for adaptive extractive summarization of covid-19 literature. arXiv preprint arXiv:2007.03405.
[5]. Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., ... & Liu, P. J. (2019). Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683.
[6]. Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.
[7]. Nallapati, R., Zhai, F., & Zhou, B. (2017, February). Summarunner: A recurrent neural network based sequence model for extractive summarization of documents. In Thirty-First AAAI Conference on Artificial Intelligence.
[8]. Narayan, S., Cohen, S. B., & Lapata, M. (2018). Ranking sentences for extractive summarization with reinforcement learning. arXiv preprint arXiv:1802.08636.
[9]. Liu, Y. (2019). Fine-tune BERT for extractive summarization. arXiv preprint arXiv:1903.10318.
[10]. Paulus, R., Xiong, C., & Socher, R. (2017). A deep reinforced model for abstractive summarization. arXiv preprint arXiv:1705.04304.
[11]. Cohan, A., Dernoncourt, F., Kim, D. S., Bui, T., Kim, S., Chang, W., & Goharian, N. (2018). A discourse-aware attention model for abstractive summarization of long documents. arXiv preprint arXiv:1804.05685.
[12]. Alammar, J. (2018, June 27). The Illustrated Transformer. From http://jalammar.github.io/illustrated-transformer/
Cite this article
Teng,Z. (2023). Abstractive summarization of COVID-19 with transfer text-to-text transformer. Applied and Computational Engineering,2,331-337.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. In Advances in neural information processing systems (pp. 5998-6008).
[2]. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
[3]. Reimers, N., & Gurevych, I. (2019). Sentence-bert: Sentence embeddings using siamese bert-networks. arXiv preprint arXiv:1908.10084.
[4]. Park, J. W. (2020). Continual bert: Continual learning for adaptive extractive summarization of covid-19 literature. arXiv preprint arXiv:2007.03405.
[5]. Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., ... & Liu, P. J. (2019). Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683.
[6]. Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.
[7]. Nallapati, R., Zhai, F., & Zhou, B. (2017, February). Summarunner: A recurrent neural network based sequence model for extractive summarization of documents. In Thirty-First AAAI Conference on Artificial Intelligence.
[8]. Narayan, S., Cohen, S. B., & Lapata, M. (2018). Ranking sentences for extractive summarization with reinforcement learning. arXiv preprint arXiv:1802.08636.
[9]. Liu, Y. (2019). Fine-tune BERT for extractive summarization. arXiv preprint arXiv:1903.10318.
[10]. Paulus, R., Xiong, C., & Socher, R. (2017). A deep reinforced model for abstractive summarization. arXiv preprint arXiv:1705.04304.
[11]. Cohan, A., Dernoncourt, F., Kim, D. S., Bui, T., Kim, S., Chang, W., & Goharian, N. (2018). A discourse-aware attention model for abstractive summarization of long documents. arXiv preprint arXiv:1804.05685.
[12]. Alammar, J. (2018, June 27). The Illustrated Transformer. From http://jalammar.github.io/illustrated-transformer/