Hierarchical text classification (HTC) is a complex subtask under multi-label
text classification, characterized by a hierarchical label taxonomy and data
imbalance. The best-performing models aim to learn a static representation by
combining document and hierarchical label information. However, the relevance
of document sections can vary based on the hierarchy level, necessitating a
dynamic document representation. To address this, we propose HiGen, a
text-generation-based framework utilizing language models to encode dynamic
text representations. We introduce a level-guided loss function to capture the
relationship between text and label name semantics. Our approach incorporates a
task-specific pretraining strategy, adapting the language model to in-domain
knowledge and significantly enhancing performance for classes with limited
examples. Furthermore, we present a new and valuable dataset called ENZYME,
designed for HTC, which comprises articles from PubMed with the goal of
predicting Enzyme Commission (EC) numbers. Through extensive experiments on the
ENZYME dataset and the widely recognized WOS and NYT datasets, our methodology
demonstrates superior performance, surpassing existing approaches while
efficiently handling data and mitigating class imbalance. The data and code
will be released publicly.

提出了一种基于语言模型的文本生成框架 HiGen 来解决多标签文本分类下的层次分类问题，通过动态文本表示方法和层级引导的损失函数，结合任务特定的预训练策略，处理数据和减轻类别不平衡问题，在 ENZYME、WOS 和 NYT 数据集上展示出优越性能。