mlsanityData to 14 Oct 2026

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

arXiv 2307.16265 ↗First posted 30 Jul 2023cs.CL cs.AIcited by 6

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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.

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

16 of 34 references.

References · 16

  1. 2016Bag of Tricks for Efficient Text Classificationcited by 876
  2. 2020Deep Learning Based Text Classification: A Comprehensive Reviewcited by 141
  3. 2018AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classificationcited by 102
  4. 2021Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classificationcited by 67
  5. 2018Weakly-Supervised Neural Text Classificationcited by 66
  6. 2020Coherent Hierarchical Multi-Label Classification Networkscited by 65
  7. 2019Bonsai -- Diverse and Shallow Trees for Extreme Multi-label Classificationcited by 61
  8. 2018A no-regret generalization of hierarchical softmax to extreme multi-label classificationcited by 52
  9. 2020An Empirical Study on Large-Scale Multi-Label Text Classification Including Few and Zero-Shot Labelscited by 44
  10. 2019Hierarchical Text Classification with Reinforced Label Assignmentcited by 41
  11. 2021Balancing Methods for Multi-label Text Classification with Long-Tailed Class Distributioncited by 35
  12. 2022HPT: Hierarchy-aware Prompt Tuning for Hierarchical Text Classificationcited by 31
4 more
  1. 2019Hyperbolic Interaction Model For Hierarchical Multi-Label Classificationcited by 30
  2. 2020LA-HCN: Label-based Attention for Hierarchical Multi-label TextClassification Neural Networkcited by 11
  3. 2022Adaptive Ranking-based Sample Selection for Weakly Supervised Class-imbalanced Text Classificationcited by 7
  4. 2022OTSeq2Set: An Optimal Transport Enhanced Sequence-to-Set Model for Extreme Multi-label Text Classificationcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 1; 2026: 1.

6 papers.

  1. 2024Can Large Language Models Serve as Effective Classifiers for Hierarchical Multi-Label Classification of Scientific Documents at Industrial Scale?cited by 11
  2. 2024Error Detection and Constraint Recovery in Hierarchical Multi-Label Classification without Prior Knowledgecited by 7
  3. 2024Walk Wisely on Graph: Knowledge Graph Reasoning with Dual Agents via Efficient Guidance-Explorationcited by 2
  4. 2024Your Next State-of-the-Art Could Come from Another Domain: A Cross-Domain Analysis of Hierarchical Text Classificationcited by 0
  5. 2025A Platform for Investigating Public Health Content with Efficient Concern Classificationcited by 0
  6. 2026AstroConcepts: A Large-Scale Multi-Label Classification Corpus for Astrophysicscited by 0