Visualizing Text Structure of Scientific Articles Using AI

Abstract

In recent years, as the number of scientific articles submitted to refereed journals has increased, the burden of peer review of researchers is increasing. The increase in peer review burden has led to delays in the publication of articles and deterioration in the quality of peer-review, and the collapse of the peer-reviewed system that has supported science is also a concern. Therefore, it is necessary to develop support technology for peer review to reduce the burden of researchers. In this research, we consider peer-review support using artificial intelligence technology. We used a Doc2Vec to numerically process the text structure of the scientific articles. We showed the differences in the text structure of the accepted and rejected manuscripts of 591 abstracts submitted to the Journal of Visualization. Furthermore, we developed a classification model of acceptance and rejection of the articles using SVM. We achieved a classification accuracy of 75% only with the abstract of the articles.