# Extreme Multi-Label Legal Text Classification: A case study in EU Legislation

arXiv: https://arxiv.org/abs/1905.10892 · HTML: https://mlsanity.com/paper/1905.10892/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2019.

## Built on by (most cited first, 26 of 26)

- [Natural Language Processing for the Legal Domain: A Survey of Tasks, Datasets, Models, and Challenges](https://mlsanity.com/paper/2410.21306/) (2024)
- [EUR-Lex-Sum: A Multi- and Cross-lingual Dataset for Long-form Summarization in the Legal Domain](https://mlsanity.com/paper/2210.13448/) (2022)
- [DeepXML: A Deep Extreme Multi-Label Learning Framework Applied to Short Text Documents](https://mlsanity.com/paper/2111.06685/) (2021)
- [NGAME: Negative Mining-aware Mini-batching for Extreme Classification](https://mlsanity.com/paper/2207.04452/) (2022)
- [Design of Negative Sampling Strategies for Distantly Supervised Skill Extraction](https://mlsanity.com/paper/2209.05987/) (2022)
- [Multi-granular Legal Topic Classification on Greek Legislation](https://mlsanity.com/paper/2109.15298/) (2021)
- [The Lucie-7B LLM and the Lucie Training Dataset: Open resources for multilingual language generation](https://mlsanity.com/paper/2503.12294/) (2025)
- [A Survey on Extreme Multi-label Learning](https://mlsanity.com/paper/2210.03968/) (2022)
- [Unsupervised Law Article Mining based on Deep Pre-Trained Language Representation Models with Application to the Italian Civil Code](https://mlsanity.com/paper/2112.03033/) (2021)
- [Automated Refugee Case Analysis: An NLP Pipeline for Supporting Legal Practitioners](https://mlsanity.com/paper/2305.15533/) (2023)
- [LexDrafter: Terminology Drafting for Legislative Documents using Retrieval Augmented Generation](https://mlsanity.com/paper/2403.16295/) (2024)
- [Efficient Text Encoders for Labor Market Analysis](https://mlsanity.com/paper/2505.24640/) (2025)
- [HSGM: Hierarchical Segment-Graph Memory for Scalable Long-Text Semantics](https://mlsanity.com/paper/2509.18168/) (2025)
- [Unsupervised Simplification of Legal Texts](https://mlsanity.com/paper/2209.00557/) (2022)
- [GAIus: Combining Genai with Legal Clauses Retrieval for Knowledge-based Assistant](https://mlsanity.com/paper/2507.01259/) (2025)
- [Using attention methods to predict judicial outcomes](https://mlsanity.com/paper/2207.08823/) (2022)
- [Empowering Refugee Claimants and their Lawyers: Using Machine Learning to Examine Decision-Making in Refugee Law](https://mlsanity.com/paper/2308.11531/) (2023)
- [The Right Model for the Job: An Evaluation of Legal Multi-Label Classification Baselines](https://mlsanity.com/paper/2401.11852/) (2024)
- [Graph Regularized Encoder Training for Extreme Classification](https://mlsanity.com/paper/2402.18434/) (2024)
- [LegalViz: Legal Text Visualization by Text To Diagram Generation](https://mlsanity.com/paper/2502.06147/) (2025)
- [Computational Identification of Regulatory Statements in EU Legislation](https://mlsanity.com/paper/2505.00479/) (2025)
- [Evaluation of Large Language Models in Legal Applications: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/2601.15267/) (2026)
- [Legal Experts Disagree With Rationale Extraction Techniques for Explaining ECtHR Case Outcome Classification](https://mlsanity.com/paper/2601.12419/) (2026)
- [Traceable by Design: An LLM Pipeline and Dashboard for EU Regulatory Consultation Analysis](https://mlsanity.com/paper/2605.30995/) (2026)
- [Does generative AI supersede supervised XMLC? A Benchmark Study on Automated Subject Indexing with German Scientific Literature](https://mlsanity.com/paper/2607.14882/) (2026)
- [Which Rules Matter Now? Policy-Centroid Routing Before an Intelligent System Acts](https://mlsanity.com/paper/2608.30757/) (2026)

## Abstract (arXiv)

We consider the task of Extreme Multi-Label Text Classification (XMTC) in the legal domain. We release a new dataset of 57k legislative documents from EURLEX, the European Union's public document database, annotated with concepts from EUROVOC, a multidisciplinary thesaurus. The dataset is substantially larger than previous EURLEX datasets and suitable for XMTC, few-shot and zero-shot learning. Experimenting with several neural classifiers, we show that BIGRUs with self-attention outperform the current multi-label state-of-the-art methods, which employ label-wise attention. Replacing CNNs with BIGRUs in label-wise attention networks leads to the best overall performance.