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We present a training framework for neural abstractive summarization based on actor-critic approaches from reinforcement learning.
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Baotian Hu, Qingcai Chen, and Fangze Zhu. 2015 · 1972
Earlier work this paper cites.
A learning algorithm for continually running fully recurrent neural networks
Ronald J Williams and David Zipser. 1989 · 1989
Earlier work this paper cites.
Finding structure in time
Jeffrey L Elman. 1990 · 1990
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams. 1992 · 1992
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
Reinforcement learning: An introduction , volume 1
Richard S Sutton and Andrew G Barto. 1998 · 1998
Earlier work this paper cites.
Actor-critic algorithms
Vijay R Konda and John N Tsitsiklis. 2000 · 2000
Earlier work this paper cites.
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Günes Erkan and Dragomir R Radev. 2004 · 2004
Earlier work this paper cites.
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Philipp Koehn. 2004 · 2004
Earlier work this paper cites.
Rouge: A package for automatic evaluation of summaries
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Later among the works it cites.
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Later among the works it cites.
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