A multitude of scholarly papers can be accessed online,
and their continual growth poses challenges in categorization. In
diverse academic fields, organizing these documents is important,
as it assists institutions, journals, and scholars in structuring their
content to improve the visibility of research. In this study, we
propose a co-training model based on transfer learning to classify
papers according to institutional research lines. We utilize cotraining
text processing techniques to enhance model learning
through transformers, enabling the identification of trends and
patterns in document texts. The model is structured with two views
(titles and abstracts) for data preprocessing and training. Each
input employs different document representation techniques that
augment its training using BERT's pre-trained scheme. For
evaluating the proposed model, a dataset comprising 898
institutional papers is compiled. These documents undergo
classification prediction in five or eleven classes, and the model
performance is compared with individually trained models from
each view using the BART pre-trained scheme and combined
models. The best precision level of 0,87 has been achieved,
compared to BERT pre-trained model's metric of 0,78 (five
classes). These findings suggest that co-training models can be a
valuable approach to improve the predictive performance of text
classification.