Multi-label document classification is a traditional task in NLP. Compared to
single-label classification, each document can be assigned multiple classes.
This problem is crucially important in various domains, such as tagging
scientific articles. Documents are often structured into several sections such
as abstract and title. Current approaches treat different sections equally for
multi-label classification. We argue that this is not a realistic assumption,
leading to sub-optimal results. Instead, we propose a new method called
Learning Section Weights (LSW), leveraging the contribution of each distinct
section for multi-label classification. Via multiple feed-forward layers, LSW
learns to assign weights to each section of, and incorporate the weights in the
prediction. We demonstrate our approach on scientific articles. Experimental
results on public (arXiv) and private (Elsevier) datasets confirm the
superiority of LSW, compared to state-of-the-art multi-label document
classification methods. In particular, LSW achieves a 1.3% improvement in terms
of macro averaged F1-score while it achieves 1.3% in terms of macro averaged
recall on the publicly available arXiv dataset.

多标签文档分类中，我们通过引入学习部分权重（LSW）的新方法，能够更准确地处理科学文章等多种领域中的文本分类任务。我们的实验结果表明，相对于现有的方法，LSW 在公开（arXiv）和私人（Elsevier）数据集上取得了显著优势，分别在宏平均 F1 分数和宏平均召回率上提升了 1.3%。