Hierarchical multi-label text classification aims to classify the input text into multiple labels, among which the labels are structured and hierarchical. It is a vital task in many real world applications, e.g. scientific literature archiving. In this paper, we survey the recent progress of hierarchical multi-label text classification, including the open sourced data sets, the main methods, evaluation metrics, learning strategies and the current challenges. A few future research directions are also listed for community to further improve this field.
@article{arxiv.2307.16265,
title = {Recent Advances in Hierarchical Multi-label Text Classification: A Survey},
author = {Rundong Liu and Wenhan Liang and Weijun Luo and Yuxiang Song and He Zhang and Ruohua Xu and Yunfeng Li and Ming Liu},
journal= {arXiv preprint arXiv:2307.16265},
year = {2023}
}