1 research outputs found
Towards the Use of Deep Reinforcement Learning with Global Policy For Query-based Extractive Summarisation
Supervised approaches for text summarisation 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.Comment: 5 pages, 2 figures, 1 algorithm. As submitted for camera ready for
the 2017 Australasian Language Technology Association Workshop (ALTA 2017