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

2017-11-10 · Diego Molla

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.

📄 PDF Abstract BibTeX arXiv:1711.03859

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dmollaaliod/alta2017-rl 공식 구현

Tasks

Deep Reinforcement Learningreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)Sentence

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

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