Learning to Extract Coherent Summary via Deep Reinforcement Learning

Abstract

Coherence plays a critical role in producing a high-quality summary from a document. In recent years, neural extractive summarization is becoming increasingly attractive. However, most of them ignore the coherence of summaries when extracting sentences. As an effort towards extracting coherent summaries, we propose a neural coherence model to capture the cross-sentence semantic and syntactic coherence patterns. The proposed neural coherence model obviates the need for feature engineering and can be trained in an end-to-end fashion using unlabeled data. Empirical results show that the proposed neural coherence model can efficiently capture the cross-sentence coherence patterns. Using the combined output of the neural coherence model and ROUGE package as the reward, we design a reinforcement learning method to train a proposed neural extractive summarizer which is named Reinforced Neural Extractive Summarization (RNES) model. The RNES model learns to optimize coherence and informative importance of the summary simultaneously. Experimental results show that the proposed RNES outperforms existing baselines and achieves state-of-the-art performance in term of ROUGE on CNN/Daily Mail dataset. The qualitative evaluation indicates that summaries produced by RNES are more coherent and readable.

8 pages, 1 figure, presented at AAAI-2018

References in corpus (5)

• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• A Deep Reinforced Model for Abstractive Summarization
• Convolutional Neural Network Architectures for Matching Natural Language Sentences
• SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents
• Get To The Point: Summarization with Pointer-Generator Networks

Cited by in corpus (5)

• Neural Abstractive Text Summarization with Sequence-to-Sequence Models
• Reinforcement Learning for Generative AI: State of the Art, Opportunities and Open Research Challenges
• DeepChannel: Salience Estimation by Contrastive Learning for Extractive Document Summarization
• Reward Learning for Efficient Reinforcement Learning in Extractive Document Summarisation
• Iterative Document Representation Learning Towards Summarization with Polishing