DeepChannel: Salience Estimation by Contrastive Learning for Extractive Document Summarization


  • Jiaxin Shi Tsinghua University
  • Chen Liang Tsinghua University
  • Lei Hou Tsinghua University
  • Juanzi Li Tsinghua University
  • Zhiyuan Liu Tsinghua University
  • Hanwang Zhang Nanyang Technological University



We propose DeepChannel, a robust, data-efficient, and interpretable neural model for extractive document summarization. Given any document-summary pair, we estimate a salience score, which is modeled using an attention-based deep neural network, to represent the salience degree of the summary for yielding the document. We devise a contrastive training strategy to learn the salience estimation network, and then use the learned salience score as a guide and iteratively extract the most salient sentences from the document as our generated summary. In experiments, our model not only achieves state-of-the-art ROUGE scores on CNN/Daily Mail dataset, but also shows strong robustness in the out-of-domain test on DUC2007 test set. Moreover, our model reaches a ROUGE-1 F-1 score of 39.41 on CNN/Daily Mail test set with merely 1/100 training set, demonstrating a tremendous data efficiency.




How to Cite

Shi, J., Liang, C., Hou, L., Li, J., Liu, Z., & Zhang, H. (2019). DeepChannel: Salience Estimation by Contrastive Learning for Extractive Document Summarization. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 6999-7006.



AAAI Technical Track: Natural Language Processing