  Open peer review is a growing trend in academic publications. Public access
to peer review data can benefit both the academic and publishing communities.
It also serves as a great support to studies on review comment generation and
further to the realization of automated scholarly paper review. However, most
of the existing peer review datasets do not provide data that cover the whole
peer review process. Apart from this, their data are not diversified enough as
the data are mainly collected from the field of computer science. These two
drawbacks of the currently available peer review datasets need to be addressed
to unlock more opportunities for related studies. In response, we construct
MOPRD, a multidisciplinary open peer review dataset. This dataset consists of
paper metadata, multiple version manuscripts, review comments, meta-reviews,
author's rebuttal letters, and editorial decisions. Moreover, we propose a
modular guided review comment generation method based on MOPRD. Experiments
show that our method delivers better performance as indicated by both automatic
metrics and human evaluation. We also explore other potential applications of
MOPRD, including meta-review generation, editorial decision prediction, author
rebuttal generation, and scientometric analysis. MOPRD is a strong endorsement
for further studies in peer review-related research and other applications.
