Recent Advances in Hierarchical Multi-label Text Classification: A
  Survey
Rundong Liu Wenhan Liang Weijun Luo Yuxiang Song He Zhang Ruohua Xu Yunfeng Li Ming Liu
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.