Automatic Academic Paper Rating Based on Convolutional Neural Network

As an ever increasing number of academic papers are being submitted to journals and conferences, assessing every one of these papers by experts is tedious and can cause imbalance because of the personal factors of the reviewers. In this system, in order to help professionals in assessing academic papers, here propose a task: Automatic Academic Paper Rating (AAPR), which automatically determine whether to accept academic papers. We build a convolutional neural network (CNN) model to achieve automatic academic paper rating task. It has two phases, first phase is identifying abstract part of source paper and generate rating score using CNN model and second phase is taking decision based on the score to accept or decline papers. This model takes word embedding of the abstracts as the input and learns useful features. The word embedding used for training the model is a semantically enriched set of Word2Vec word embedding. After the training phase, the proposed model will be able to generate the score of a new abstract. And find that the title and abstract parts have the most influence on whether the source paper quality when setting aside the other part of source papers. The proposed system outperforms the state-of-art technique.