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Recent work pre-training Transformers with self-supervised objectives on large text corpora has shown great success when fine-tuned on downstream NLP tasks including text summarization.
Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
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English gigaword
Graff, D., Kong, J., Chen, K., and Maeda, K · 2003
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The enron corpus: A new dataset for email classification research
Klimt, B. and Yang, Y · 2004
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ROUGE: A package for automatic evaluation of summaries
Lin, C.-Y · 2004
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Empirical evaluation of gated recurrent neural networks on sequence modeling
Chung, J., Gulcehre, C., Cho, K., and Bengio, Y · 2014
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Sequence to sequence learning with neural networks
Sutskever, I., Vinyals, O., and Le, Q. V · 2014
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Semi-supervised sequence learning
Dai, A. M. and Le, Q. V · 2015
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Teaching machines to read and comprehend
Hermann, K. M., Kocisky, T., Grefenstette, E., Espeholt, L., Kay, W., Suleyman, M., and Blunsom, P · 2015
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A neural attention model for abstractive sentence summarization
Rush, A. M., Chopra, S., and Weston, J · 2015
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Abstractive text summarization using sequence-to-sequence RNNs and beyond
Nallapati, R., Zhou, B., dos Santos, C., Gu̇lçehre, Ç., and Xiang, B · 2016
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Squad: 100,000+ questions for machine comprehension of text
Rajpurkar, P., Zhang, J., Lopyrev, K., and Liang, P · 2016
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Neural machine translation of rare words with subword units
Sennrich, R., Haddow, B., and Birch, A · 2016
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Google’s neural machine translation system: Bridging the gap between human and machine translation
Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., et al · 2016
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Summarunner: A recurrent neural network based sequence model for extractive summarization of documents
Nallapati, R., Zhai, F., and Zhou, B · 2017
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A deep reinforced model for abstractive summarization
Paulus, R., Xiong, C., and Socher, R · 2017
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Unsupervised pretraining for sequence to sequence learning
Ramachandran, P., Liu, P., and Le, Q · 2017
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Get to the point: Summarization with pointer-generator networks
See, A., Liu, P. J., and Manning, C. D · 2017
Cited alongside, same era.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Cited alongside, same era.
TL;DR: Mining Reddit to learn automatic summarization
Völske, M., Potthast, M., Syed, S., and Stein, B · 2017
Cited alongside, same era.
Newsroom: A dataset of 1.3 million summaries with diverse extractive strategies
Grusky, M., Naaman, M., and Artzi, Y · 2018
Cited alongside, same era.
Wikihow: A large scale text summarization dataset
Koupaee, M. and Wang, W. Y · 2018
Cited alongside, same era.
Abstractive summarization of Reddit posts with multi-level memory networks
Kim, B., Kim, H., and Kim, G · 2019
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BillSum: A corpus for automatic summarization of US legislation
Kornilova, A. and Eidelman, V · 2019
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Neural text summarization: A critical evaluation
Kryscinski, W., Keskar, N. S., McCann, B., Xiong, C., and Socher, R · 2019
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Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., and Zettlemoyer, L · 2019
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Exploring the limits of transfer learning with a unified text-to-text transformer, 2019
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., and Liu, P. J · 2019
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Kudo, T · 2018
Cited alongside, same era.
Generating wikipedia by summarizing long sequences
Liu, P. J., Saleh, M., Pot, E., Goodrich, B., Sepassi, R., Kaiser, L., and Shazeer, N · 2018
Cited alongside, same era.
Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization
Narayan, S., Cohen, S. B., and Lapata, M · 2018
Cited alongside, same era.
Adafactor: Adaptive learning rates with sublinear memory cost
Shazeer, N. and Stern, M · 2018
Cited alongside, same era.
Glue: A multi-task benchmark and analysis platform for natural language understanding
Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., and Bowman, S · 2018
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2019
Cited alongside, same era.
Unified language model pre-training for natural language understanding and generation
Dong, L., Yang, N., Wang, W., Wei, F., Liu, X., Wang, Y., Gao, J., Zhou, M., and Hon, H.-W · 2019
Cited alongside, same era.
Rothe, S., Narayan, S., and Severyn, A · 2019
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BIGPATENT: A large-scale dataset for abstractive and coherent summarization
Sharma, E., Li, C., and Wang, L · 2019
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LeafNATS: An open-source toolkit and live demo system for neural abstractive text summarization
Shi, T., Wang, P., and Reddy, C. K · 2019
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Mass: Masked sequence to sequence pre-training for language generation
Song, K., Tan, X., Qin, T., Lu, J., and Liu, T.-Y · 2019
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On extractive and abstractive neural document summarization with transformer language models
Subramanian, S., Li, R., Pilault, J., and Pal, C · 2019
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Neural text generation with unlikelihood training
Welleck, S., Kulikov, I., Roller, S., Dinan, E., Cho, K., and Weston, J · 2019
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Xlnet: Generalized autoregressive pretraining for language understanding
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., and Le, Q. V · 2019
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This email could save your life: Introducing the task of email subject line generation
Zhang, R. and Tetreault, J · 2019
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Searching for effective neural extractive summarization: What works and what’s next
Zhong, M., Liu, P., Wang, D., Qiu, X., and Huang, X · 2019
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A discourse-aware attention model for abstractive summarization of long documents
Cohan, A., Dernoncourt, F., Kim, D. S., Bui, T., Kim, S., Chang, W., and Goharian, N · 2097
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