Papers › SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization
SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization
Yixin Liu, PengFei Liu
In this paper, we present a conceptually simple while empirically powerful framework for abstractive summarization, SimCLS, which can bridge the gap between the learning objective and evaluation metrics resulting from the currently dominated sequence-to-sequence learning framework by formulating text generation as a reference-free evaluation problem (i.e., quality estimation) assisted by contrastive learning. Experimental results show that, with minor modification over existing top-scoring systems, SimCLS can improve the performance of existing top-performing models by a large margin. Particularly, 2.51 absolute improvement against BART and 2.50 over PEGASUS w.r.t ROUGE-1 on the CNN/DailyMail dataset, driving the state-of-the-art performance to a new level. We have open-sourced our codes and results: https://github.com/yixinL7/SimCLS. Results of our proposed models have been deployed into ExplainaBoard platform, which allows researchers to understand our systems in a more fine-grained way.
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Abstractive Text Summarization | CNN / Daily Mail | BART + SimCLS | ROUGE-1 | 46.67 | #6 of 53 | Archive leaderboard | report |
| Abstractive Text Summarization | CNN / Daily Mail | BART + SimCLS | ROUGE-2 | 22.15 | #6 of 53 | Archive leaderboard | report |
| Abstractive Text Summarization | CNN / Daily Mail | BART + SimCLS | ROUGE-L | 43.54 | #6 of 53 | Archive leaderboard | report |
| Text Summarization | X-Sum | PEGASUS + SimCLS | ROUGE-1 | 47.61 | #4 of 18 | Archive leaderboard | report |
| Text Summarization | X-Sum | PEGASUS + SimCLS | ROUGE-2 | 24.57 | #4 of 18 | Archive leaderboard | report |
| Text Summarization | X-Sum | PEGASUS + SimCLS | ROUGE-L | 39.44 | #4 of 18 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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