Utilizing Maximum Entropy and Transformer Models for Automatic Academic Writing Abstract Classification Algorithm

This paper presents a novel hybrid model that seamlessly integrates the Maximum Entropy (MaxEnt) model with the Transformer architecture for the automatic classification of academic abstracts. The MaxEnt model plays a crucial role in efficiently managing diverse and sparse feature sets, employing a probabilistic framework to focus on the most informative attributes in the data. Meanwhile, the Transformer model excels in capturing intricate semantic relationships within the text, utilizing its advanced self-attention mechanism to decode complex dependencies and contextual meanings. The synergy of these two models allows the MaxEnt-Transformer to achieve a more holistic understanding of academic abstracts, effectively balancing detailed feature extraction with deep semantic interpretation. Our proposed model was thoroughly evaluated on the ArXiv and PubMed datasets, where it demonstrated superior performance compared to both traditional models, such as CNN and LSTM, and more advanced models like BERT. The MaxEnt-Transformer model not only achieved a significant improvement in classification accuracy but also exhibited a reduced prediction error, underscoring its effectiveness and robustness in academic text classification tasks. This hybrid approach offers a promising and scalable solution for the automatic management of large volumes of academic literature, addressing the increasing demand for efficient classification tools in scholarly research and knowledge dissemination.