As more and more academic papers are being submitted to conferences and journals, evaluating all these papers by professionals is time-consuming and can cause inequality due to the personal factors of the reviewers. In this paper, in order to assist professionals in evaluating academic papers, we propose a novel task: automatic academic paper rating (AAPR), which automatically determine whether to accept academic papers. We build a new dataset for this task and propose a novel modularized hierarchical convolutional neural network to achieve automatic academic paper rating. Evaluation results show that the proposed model outperforms the baselines by a large margin. The dataset and code are available at \url{https://github.com/lancopku/AAPR}
@article{arxiv.1805.03977,
title = {Automatic Academic Paper Rating Based on Modularized Hierarchical Convolutional Neural Network},
author = {Pengcheng Yang and Xu Sun and Wei Li and Shuming Ma},
journal= {arXiv preprint arXiv:1805.03977},
year = {2018}
}