Long Text Classification

Long text classification aims to automatically categorize lengthy documents, a challenging task due to computational constraints and the need to effectively capture relevant information within extensive text. Current research focuses on improving efficiency and accuracy using transformer-based models like BERT, often incorporating techniques such as chunking, selective attention mechanisms, and optimized pre-processing to handle the quadratic complexity associated with long sequences. These advancements are crucial for various applications, including medical record analysis and news categorization, where efficient and accurate processing of large volumes of textual data is essential.