# Recent Advances in Hierarchical Multi-label Text Classification: A Survey

arXiv: https://arxiv.org/abs/2307.16265 · HTML: https://mlsanity.com/paper/2307.16265/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 34 references that are in the map.
- First posted on arXiv 30 Jul 2023.

## Builds on (most cited first, 16 of 16)

- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Deep Learning Based Text Classification: A Comprehensive Review](https://mlsanity.com/paper/2004.03705/) (2020)
- [AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification](https://mlsanity.com/paper/1811.01727/) (2018)
- [Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification](https://mlsanity.com/paper/2110.00685/) (2021)
- [Weakly-Supervised Neural Text Classification](https://mlsanity.com/paper/1809.01478/) (2018)
- [Coherent Hierarchical Multi-Label Classification Networks](https://mlsanity.com/paper/2010.10151/) (2020)
- [Bonsai -- Diverse and Shallow Trees for Extreme Multi-label Classification](https://mlsanity.com/paper/1904.08249/) (2019)
- [A no-regret generalization of hierarchical softmax to extreme multi-label classification](https://mlsanity.com/paper/1810.11671/) (2018)
- [An Empirical Study on Large-Scale Multi-Label Text Classification Including Few and Zero-Shot Labels](https://mlsanity.com/paper/2010.01653/) (2020)
- [Hierarchical Text Classification with Reinforced Label Assignment](https://mlsanity.com/paper/1908.10419/) (2019)
- [Balancing Methods for Multi-label Text Classification with Long-Tailed Class Distribution](https://mlsanity.com/paper/2109.04712/) (2021)
- [HPT: Hierarchy-aware Prompt Tuning for Hierarchical Text Classification](https://mlsanity.com/paper/2204.13413/) (2022)
- [Hyperbolic Interaction Model For Hierarchical Multi-Label Classification](https://mlsanity.com/paper/1905.10802/) (2019)
- [LA-HCN: Label-based Attention for Hierarchical Multi-label TextClassification Neural Network](https://mlsanity.com/paper/2009.10938/) (2020)
- [Adaptive Ranking-based Sample Selection for Weakly Supervised Class-imbalanced Text Classification](https://mlsanity.com/paper/2210.03092/) (2022)
- [OTSeq2Set: An Optimal Transport Enhanced Sequence-to-Set Model for Extreme Multi-label Text Classification](https://mlsanity.com/paper/2210.14523/) (2022)

## Built on by (most cited first, 6 of 6)

- [Can Large Language Models Serve as Effective Classifiers for Hierarchical Multi-Label Classification of Scientific Documents at Industrial Scale?](https://mlsanity.com/paper/2412.05137/) (2024)
- [Error Detection and Constraint Recovery in Hierarchical Multi-Label Classification without Prior Knowledge](https://mlsanity.com/paper/2407.15192/) (2024)
- [Walk Wisely on Graph: Knowledge Graph Reasoning with Dual Agents via Efficient Guidance-Exploration](https://mlsanity.com/paper/2408.01880/) (2024)
- [Your Next State-of-the-Art Could Come from Another Domain: A Cross-Domain Analysis of Hierarchical Text Classification](https://mlsanity.com/paper/2412.12744/) (2024)
- [A Platform for Investigating Public Health Content with Efficient Concern Classification](https://mlsanity.com/paper/2506.01308/) (2025)
- [AstroConcepts: A Large-Scale Multi-Label Classification Corpus for Astrophysics](https://mlsanity.com/paper/2604.02156/) (2026)

## Abstract (arXiv)

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.