We present a training framework for neural abstractive summarization based on
actor-critic approaches from reinforcement learning. In the traditional neural
network based methods, the objective is only to maximize the likelihood of the
predicted summaries, no other assessment constraints are considered, which may
generate low-quality summaries or even incorrect sentences. To alleviate this
problem, we employ an actor-critic framework to enhance the training procedure.
For the actor, we employ the typical attention based sequence-to-sequence
(seq2seq) framework as the policy network for summary generation. For the
critic, we combine the maximum likelihood estimator with a well designed global
summary quality estimator which is a neural network based binary classifier
aiming to make the generated summaries indistinguishable from the human-written
ones. Policy gradient method is used to conduct the parameter learning. An
alternating training strategy is proposed to conduct the joint training of the
actor and critic models. Extensive experiments on some benchmark datasets in
different languages show that our framework achieves improvements over the
state-of-the-art methods.

介绍使用强化学习的演员 - 评论家框架作为神经网络摘要生成模型培训框架，框架中包括政策网络、二进制分类器、修改的最大近似似然度量和参数学习方法，该框架达到了比现有最优秀方法更好的效果。