(2018) recently proposed an extractive summarization approach based on deep Q learning, a type of reinforcement learning.
empirical results show that our method performs better than or comparable to state-of-the-art extractive summarization models which must be pre-trained on extractive labels, and converges using significantly fewer update steps than competing approaches.