The first iteration of the JUST-NLP: Workshop on NLP for Empowering Justice was organized to accelerate research in Natural Language Processing for legal text processing. The inaugural edition, JUST-NLP 2025, was held as a hybrid event at IJCNLP-AACL 2025 on December 24 at IIT Bombay. The program featured a research track, four invited talks, and two shared tasks: (1) L-SUMM, an abstractive summarization task for Indian legal judgments, and (2) L-MT, a legal machine translation task between English and Hindi. The workshop received strong interest from the community, with 29 submissions, of which 21 were accepted. Among the accepted papers, 5 were regular research-track papers published in the proceedings, and 2 were accepted as non-archival presentations. For the shared tasks, 9 papers were accepted for L-SUMM, and 5 papers were accepted for L-MT, for publication in the proceedings. The workshop focused on a broad set of Legal NLP challenges, including information extraction, retrieval, multilingual processing, legal reasoning, and applications of large language models. Overall, JUST-NLP 2025 aimed to bring together AI researchers and legal practitioners to develop scalable, domain-aware NLP methods that can support legal workflows and contribute toward more efficient and equitable justice systems.

This paper presents an overview of the Shared Task on Summarization of Indian Court Judgments (L-SUMM), hosted by the JUST-NLP 2025 Workshop at IJCNLP-AACL 2025. This task aims to increase research interest in automatic summarization techniques for lengthy and intricate legal documents from the Indian judiciary. It particularly addresses court judgments that contain dense legal reasoning and semantic roles that must be preserved in summaries. As part of this shared task, we introduce the Indian Legal Summarization (L-SUMM) dataset, comprising 1,800 Indian court judgments paired with expert-written abstractive summaries, both in English. Therefore, the task focuses on generating high-quality abstractive summaries of court judgments in English. A total of 9 teams participated in this task, exploring a diverse range of methodologies, including transformer-based models, extractive-abstractive hybrids, graph-based ranking approaches, long-context LLMs, and rhetorical-role-based techniques. This paper describes the task setup, dataset, evaluation framework, and our findings. We report the results and highlight key trends across participant approaches, including the effectiveness of hybrid pipelines and challenges in handling extreme sequence lengths.

This paper provides an overview of the Shared Task on Legal Machine Translation (L-MT), organized as part of the JUST-NLP 2025 Workshop at IJCNLP-AACL 2025, aimed at improving the translation of legal texts, a domain where precision, structural faithfulness, and terminology preservation are essential. The training set comprises 50,000 sentences, with 5,000 sentences each for the validation and test sets. The submissions employed strategies such as: domain-adaptive fine-tuning of multilingual models, QLoRA-based parameter-efficient adaptation, curriculum-guided supervised training, reinforcement learning with verifiable MT metrics, and from-scratch Transformer training. The systems are evaluated based on BLEU, METEOR, TER, chrF++, BERTScore, and COMET metrics. We also combine the scores of these metrics to give an average score (AutoRank). The top-performing system is based on a fine-tuned distilled NLLB-200 model and achieved the highest AutoRank score of 72.1. Domain adaptation consistently yielded substantial improvements over baseline models, and precision-focused rewards proved especially effective for the legal MT. The findings also highlight that large multilingual Transformers can deliver accurate and reliable English-to-Hindi legal translations when carefully fine-tuned on legal data, advancing the broader goal of improving access to justice in multilingual settings.