Text classification is an area of research which has been studied over the years in Natural Language Processing (NLP). Adapting NLP to multiple domains has introduced many new challenges for text classification and one of them is long document classification. While state-of-the-art transformer models provide excellent results in text classification, most of them have limitations in the maximum sequence length of the input sequence. The majority of the transformer models are limited to 512 tokens, and therefore, they struggle with long document classification problems. In this research, we explore on employing Model Fusing for long document classification while comparing the results with well-known BERT and Longformer architectures.

翻译：文本分类是自然语言处理(NLP)领域中一个历经多年研究的重要方向。将NLP适配到多个领域为文本分类带来了诸多新挑战，其中之一便是长文档分类。尽管当前最先进的Transformer模型在文本分类中表现出色，但大多数模型在输入序列的最大长度上存在限制。多数Transformer模型局限于512个标记(token)，因此难以应对长文档分类问题。本研究探索了采用模型融合(Model Fusing)方法进行长文档分类，并与知名的BERT和Longformer架构进行了结果对比。