2017 ADeepReinforcedModelforAbstract

• (Paulus et al., 2017) ⇒ Romain Paulus, Caiming Xiong, and Richard Socher. (2017). “A Deep Reinforced Model for Abstractive Summarization.” arXiv:1705.04304

Subject Headings: Attention Mechanism, Abstractive Summarization.

Cited By

• http://scholar.google.com/scholar?q=%222017%22+A+Deep+Reinforced+Model+for+Abstractive+Summarization

Quotes

Abstract

Attentional, RNN-based encoder-decoder models for abstractive summarization have achieved good performance on short input and output sequences. For longer documents and summaries however these models often include repetitive and incoherent phrases. We introduce a neural network model with a novel intra-attention that attends over the input and continuously generated output separately, and a new training method that combines standard supervised word prediction and reinforcement learning (RL). Models trained only with supervised learning often exhibit " exposure bias " - they assume ground truth is provided at each step during training. However, when standard word prediction is combined with the global sequence prediction training of RL the resulting summaries become more readable. We evaluate this model on the CNN / Daily Mail and New York Times datasets. Our model obtains a 41.16 ROUGE-1 score on the CNN / Daily Mail dataset, an improvement over previous state-of-the-art models. Human evaluation also shows that our model produces higher quality summaries.

References

;

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2017 ADeepReinforcedModelforAbstract	Richard Socher Caiming Xiong Romain Paulus			A Deep Reinforced Model for Abstractive Summarization