  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.
