“Towards the Use of Deep Reinforcement Learning With Global Policy For Query-Based Extractive Summarization”, 2017-11-10 (; backlinks; similar):
Supervised approaches for text summarization suffer from the problem of mismatch between the target labels/scores of individual sentences and the evaluation score of the final summary. Reinforcement learning can solve this problem by providing a learning mechanism that uses the score of the final summary as a guide to determine the decisions made at the time of selection of each sentence.
In this paper we present a proof-of-concept approach that applies a policy-gradient algorithm to learn a stochastic policy using an undiscounted reward.
The method has been applied to a policy consisting of a simple neural network and simple features.
The resulting deep reinforcement learning system is able to learn a global policy and obtain encouraging results.