Before revision:  In this paper, we propose Semantic Similarity strategy that can consider semantic meanings of generated summaries while training. Our training objective includes maximizing semantic similarity score which is calculated by an additional layer that estimates semantic similarity between generated summary and reference summary. By leveraging pre-trained language models, our model achieves a new state-of-the-art performance, ROUGE-L score of 41.5 on CNN/DM dataset.
After revision:  In this paper, we compare the generated summaries from recent LM, BART, and the reference summaries from a benchmark dataset, CNN/DM dataset.