isaacus/irish-legislative-summaries

Irish Legislative Summaries ⚖️

Irish Legislative Summaries by Isaacus is a novel, challenging legal information retrieval evaluation dataset consisting of 500 Irish laws and their long titles, succinctly summarizing subject matter, scope, and purpose of legislation.

This dataset is meant to stress test the ability of an information retrieval model to retrieve relevant statutes to short queries describing them.

This dataset forms part of the Massive Legal Embeddings Benchmark (MLEB), the largest, most diverse, and most comprehensive benchmark for legal text embedding models.

Structure 🗂️

As per the MTEB information retrieval dataset format, this dataset comprises three splits, default, corpus, and queries.

The default split pairs long titles (query-id) with legislation (corpus-id), each pair having a score of 1.

The corpus split contains Irish legislation (excluding long titles and preceding metadata), with the text of acts being stored in the text key and their IDs being stored in the _id key. There is also a title column which is deliberately set to an empty string in all cases for compatibility with the mteb library.

The queries split contains long titles in the format 'An Act ...', with the text of a long title being stored in the text key and its ID being stored in the _id key.

Methodology 🧪

This dataset was constructed by collecting all publicly available Irish laws, converting them into plain text with Inscriptis, cleaning them and removing near duplicates with the simhash algorithm, and then using regex to extract their long titles before removing those long titles and everything preceding them from legislation (in order to force models to focus on representing the core semantics of acts' texts rather than their metadata-rich front matter). Finally, 500 statute-long title pairs were randomly selected for inclusion in this dataset.

License 📜

This dataset is licensed under CC BY 4.0 which permits both non-commercial and commercial use of this dataset provided appropriate attribution is made to it.

Citation 🔖

If you use this dataset, please cite the Massive Legal Embeddings Benchmark (MLEB):

@misc{butler2025massivelegalembeddingbenchmark,
      title={The Massive Legal Embedding Benchmark (MLEB)}, 
      author={Umar Butler and Abdur-Rahman Butler and Adrian Lucas Malec},
      year={2025},
      eprint={2510.19365},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2510.19365}, 
}