  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. In this paper, we compare
the generated summaries from recent LM, BART, and the reference summaries from
a benchmark dataset, CNN/DM, using a crowd-sourced human evaluation metric.
Interestingly, model-generated summaries receive higher scores relative to
reference summaries. Stemming from our experimental results, we first argue the
intrinsic characteristics of the CNN/DM dataset, the progress of pre-trained
language models, and their ability to generalize on the training data. Finally,
we share our insights into the model-generated summaries and presents our
thought on learning methods for abstractive summarization.
