SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization

• URL: http://arxiv.org/abs/2106.01890v1
• Date: Thu, 3 Jun 2021 14:34:17 GMT
• Title: SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization
• Authors: Yixin Liu, Pengfei Liu
• Abstract summary: We present a conceptually simple while empirically powerful framework for abstractive summarization Sim. With minor modification over existing top-scoring systems, Sim can improve the performance of existing top-performing systems by a large margin. Results of our proposed models have been deployed into ExplainaBoard platform, which allows researchers to understand our systems in a more fine-grained way.
• Score: 14.16710715347118
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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