Assessing the Quality of Scientific Articles Using Artificial Neural Networks

Assessing the quality of scientific articles being submitted for publishing through digital libraries is an essential step that ensures that the published articles meet the qualifications required by the journal, to maintain the reputations of these journals. Normally, these articles are peer-reviewed by experts in the field the article investigates, which has dramatically increased the time required to assess these articles before publishing. The quality of an article is measured mainly by the quality of the writing and the significance of the field it investigates. In this study, a quality assessment technique is proposed, which uses artificial neural networks to predict the number of citations the article is expected to gain as a measure of its quality. The evaluation is based on three main components of the article, the title, keywords and abstract, as these components can reflect the overall quality of the article, without the need of excessive processing of the entire article. Two approaches are evaluated, the first attempt to measure the quality per each of the components, separately, and fuse the measures into a single overall quality measure. The second approach uses a single hybrid neural network that combines the three networks together, so that, these components are processed simultaneously. Per each approach, three types of neural networks are evaluated, which are the Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long-Short-Term Memory (LSTM). The results show that the use of the CNN with the hybrid network has achieved the best predictions, with 4.52 Mean Squared Error (MSE).