Before revision: One of the obstacles of abstractive summarization is the presence of various potentially correct predictions. Widely used objective functions for supervised learning, such as cross-entropy loss, cannot handle alternative answers effectively. Rather, they act as a training noise .
After revision: By harnessing pre-trained language models, summarization models had rapid progress recently. However, the models are mainly assessed by automatic evaluation metrics such as ROUGE. Although ROUGE is known for having a positive correlation with human evaluation scores, it has been criticized for its vulnerability and the gap between actual qualities .