# DeepChannel: Salience Estimation by Contrastive Learning for Extractive Document Summarization

arXiv: https://arxiv.org/abs/1811.02394 · HTML: https://mlsanity.com/paper/1811.02394/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2018.

## Built on by (most cited first, 6 of 6)

- [Black-box Adversarial Attacks against Dense Retrieval Models: A Multi-view Contrastive Learning Method](https://mlsanity.com/paper/2308.09861/) (2023)
- [Supervised Contrastive Learning with Hard Negative Samples](https://mlsanity.com/paper/2209.00078/) (2022)
- [Propagation Tree Is Not Deep: Adaptive Graph Contrastive Learning Approach for Rumor Detection](https://mlsanity.com/paper/2508.07201/) (2025)
- [WikiDes: A Wikipedia-Based Dataset for Generating Short Descriptions from Paragraphs](https://mlsanity.com/paper/2209.13101/) (2022)
- [Harnessing Contrastive Learning and Neural Transformation for Time Series Anomaly Detection](https://mlsanity.com/paper/2304.07898/) (2023)
- [Reinforcing Semantic-Symmetry for Document Summarization](https://mlsanity.com/paper/2112.07583/) (2021)

## Abstract (arXiv)

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.