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Abstractive summarization has been studied using neural sequence transduction methods with datasets of large, paired document-summary examples.
A learning algorithm for continually running fully recurrent neural networks
Williams, R. J. and Zipser, D · 1989
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Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
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Using n-grams to understand the nature of summaries
Banko, M. and Vanderwende, L · 2004
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Rouge: A package for automatic evaluation of summaries
Lin, C.-Y · 2004
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Centroid-based summarization of multiple documents
Radev, D. R., Jing, H., Styś, M., and Tam, D · 2004
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Overview of duc 2005
Dang, H. T · 2005
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Opinion observer: analyzing and comparing opinions on the web
Liu, B., Hu, M., and Cheng, J · 2005
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The impact of frequency on summarization
Nenkova, A. and Vanderwende, L · 2005
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Movie review mining and summarization
Zhuang, L., Jing, F., and Zhu, X.-Y · 2006
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Product review summarization from a deeper perspective
Ly, D. K., Sugiyama, K., Lin, Z., and Kan, M.-Y · 2011
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Efficient estimation of word representations in vector space
Mikolov, T., Chen, K., Corrado, G., and Dean, J · 2013
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Convolutional neural networks for sentence classification
Kim, Y · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Sequence to sequence learning with neural networks
Sutskever, I., Vinyals, O., and Le, Q. V · 2014
Cited alongside, same era.
Toward abstractive summarization using semantic representations
Liu, F., Flanigan, J., Thomson, S., Sadeh, N., and Smith, N. A · 2015
Cited alongside, same era.
Image-based recommendations on styles and substitutes
McAuley, J., Targett, C., Shi, Q., and Van Den Hengel, A · 2015
Cited alongside, same era.
Sequence level training with recurrent neural networks
Ranzato, M., Chopra, S., Auli, M., and Zaremba, W · 2015
Cited alongside, same era.
A neural attention model for abstractive sentence summarization
Unsupervised pretraining for sequence to sequence learning
Ramachandran, P., Liu, P. J., and Le, Q. V · 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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Unsupervised neural machine translation
Artetxe, M., Labaka, G., Agirre, E., and Cho, K · 2017
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State-of-the-art speech recognition with sequence-to-sequence models
Chiu, C.-C., Sainath, T. N., Wu, Y., Prabhavalkar, R., Nguyen, P., Chen, Z., Kannan, A., Weiss, R. J., Rao, K., Gonina, K., et al · 2017
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Rush, A. M., Chopra, S., and Weston, J · 2015
Cited alongside, same era.
Ba, J. L., Kiros, J. R., and Hinton, G. E · 2016
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Categorical reparameterization with gumbel-softmax
Jang, E., Gu, S., and Poole, B · 2016
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Multiplicative lstm for sequence modelling
Krause, B., Lu, L., Murray, I., and Renals, S · 2016
Cited alongside, same era.
The concrete distribution: A continuous relaxation of discrete random variables
Maddison, C. J., Mnih, A., and Teh, Y. W · 2016
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Language as a latent variable: Discrete generative models for sentence compression
Miao, Y. and Blunsom, P · 2016
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Abstractive text summarization using sequence-to-sequence rnns and beyond
Nallapati, R., Zhou, B., dos Santos, C., glar Gulçehre, Ç., and Xiang, B · 2016
Cited alongside, same era.
Lample, G., Denoyer, L., and Ranzato, M · 2017
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Centroid-based text summarization through compositionality of word embeddings
Rossiello, G., Basile, P., and Semeraro, G · 2017
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Get to the point: Summarization with pointer-generator networks
See, A., Liu, P. J., and Manning, C. D · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., and Efros, A. A · 2017
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Unsupervised cipher cracking using discrete gans
Gomez, A. N., Huang, S., Zhang, I., Li, B. M., Osama, M., and Kaiser, L · 2018
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Generating wikipedia by summarizing long sequences
Liu, P. J., Saleh, M., Pot, E., Goodrich, B., Sepassi, R., Kaiser, L., and Shazeer, N · 2018
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Learning to encode text as human-readable summaries using generative adversarial networks
Wang, Y. and Lee, H.-y · 2018
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