Named Entity Recognition for peer-review disambiguation in academic publishing

In recent years, there has been a constant increase in the number of scientific peer-reviewed articles published. Each of these articles has to go through a laborious process, from peer review, through author revision rounds, to the final decision made by the editor-in-chief. Lacking time and being under pressure with diverse research tasks, senior scientists need new tools to automate parts of their activities. In this paper, we propose a new approach based on named entity recognition that is able to annotate review comments in order to extract meaningful information about changes requested by reviewers. This research focuses on deep learning models that are achieving state-of-the-art results in many natural language processing tasks. Exploring the performance of BERT-based and XLNet models on the review comments annotation task, a “review-annotation“ model based on SciBERT was trained, able to achieve an F1 score of 0.87. Its usage allows different players in the academic publishing process to better understand the review request. In addition, the correlation of the requested and the actual changes is made possible, allowing the final decision-maker to strengthen the article evaluation.