@inproceedings{borcin-jose-2026-firac,
title = "{FIRAC}-{APPEALS}: A Structured {I}ndian Appellate Judgments Dataset for Summarisation and Reasoning-Aware Legal {NLP}",
author = "Bor{\v{c}}in, Martin and
Jose, Joemon M.",
editor = "Aletras, Nikolaos and
Chalkidis, Ilias and
Barrett, Leslie and
Goanț{\u{a}}, C{\u{a}}t{\u{a}}lina and
Preoțiuc-Pietro, Daniel and
Spanakis, Gerasimos and
Xu, Shanshan",
booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.nllp-1.36/",
pages = "420--431",
ISBN = "979-8-89176-494-1",
abstract = "Legal natural language processing (NLP) resources often flatten judgments into full texts paired with summaries or outcome labels, despite appellate reasoning being organised around facts, issues, authorities, analysis, and dispositions. We introduce , a public dataset and annotation pipeline that converts 45,020 Indian Supreme Court appeals into FIRAC-style case briefs with metadata, leakage-controlled Facts, typed Rules/Authorities, Analysis, Conclusion and outcome labels. This structure supports headnote generation, issue spotting, authority recommendation, reasoning-aware audit, and leakage-aware outcome prediction. Human legal evaluation, large language model (LLM) screening, summarisation comparisons, and downstream baselines demonstrate the dataset{'}s usability beyond conventional summarisation or outcome-only legal judgment prediction, providing a practical foundation for more structured and inspectable legal NLP."
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%0 Conference Proceedings
%T FIRAC-APPEALS: A Structured Indian Appellate Judgments Dataset for Summarisation and Reasoning-Aware Legal NLP
%A Borčin, Martin
%A Jose, Joemon M.
%Y Aletras, Nikolaos
%Y Chalkidis, Ilias
%Y Barrett, Leslie
%Y Goanță, Cătălina
%Y Preoțiuc-Pietro, Daniel
%Y Spanakis, Gerasimos
%Y Xu, Shanshan
%S Proceedings of the Natural Legal Language Processing Workshop 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-494-1
%F borcin-jose-2026-firac
%X Legal natural language processing (NLP) resources often flatten judgments into full texts paired with summaries or outcome labels, despite appellate reasoning being organised around facts, issues, authorities, analysis, and dispositions. We introduce , a public dataset and annotation pipeline that converts 45,020 Indian Supreme Court appeals into FIRAC-style case briefs with metadata, leakage-controlled Facts, typed Rules/Authorities, Analysis, Conclusion and outcome labels. This structure supports headnote generation, issue spotting, authority recommendation, reasoning-aware audit, and leakage-aware outcome prediction. Human legal evaluation, large language model (LLM) screening, summarisation comparisons, and downstream baselines demonstrate the dataset’s usability beyond conventional summarisation or outcome-only legal judgment prediction, providing a practical foundation for more structured and inspectable legal NLP.
%U https://aclanthology.org/2026.nllp-1.36/
%P 420-431
Markdown (Informal)
[FIRAC-APPEALS: A Structured Indian Appellate Judgments Dataset for Summarisation and Reasoning-Aware Legal NLP](https://aclanthology.org/2026.nllp-1.36/) (Borčin & Jose, NLLP 2026)
ACL