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Supervised approaches for text summarisation suffer from the problem ofmismatch between the target labels/scores of individual sentences and theevaluation score of the final summary. Reinforcement learning can solve thisproblem by providing a learning mechanism that uses the score of the finalsummary as a guide to determine the decisions made at the time of selection ofeach sentence. In this paper we present a proof-of-concept approach thatapplies a policy-gradient algorithm to learn a stochastic policy using anundiscounted reward. The method has been applied to a policy consisting of asimple neural network and simple features. The resulting deep reinforcementlearning system is able to learn a global policy and obtain encouragingresults.

Towards the Use of Deep Reinforcement Learning with Global Policy For Query-based Extractive Summarisation