@inproceedings{datta-etal-2025-findings,
    title = "Findings of the {JUST}-{NLP} 2025 Shared Task on Summarization of {I}ndian Court Judgments",
    author = "Datta, Debtanu  and
      Paul, Shounak  and
      Singh, Kshetrimayum Boynao  and
      Kumar, Sandeep  and
      Joshi, Abhinav  and
      Mishra, Shivani  and
      Jain, Sarika  and
      Ekbal, Asif  and
      Goyal, Pawan  and
      Modi, Ashutosh  and
      Ghosh, Saptarshi",
    editor = "Modi, Ashutosh  and
      Ghosh, Saptarshi  and
      Ekbal, Asif  and
      Goyal, Pawan  and
      Jain, Sarika  and
      Joshi, Abhinav  and
      Mishra, Shivani  and
      Datta, Debtanu  and
      Paul, Shounak  and
      Singh, Kshetrimayum Boynao  and
      Kumar, Sandeep",
    booktitle = "Proceedings of the 1st Workshop on NLP for Empowering Justice (JUST-NLP 2025)",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.justnlp-main.2/",
    pages = "5--11",
    ISBN = "979-8-89176-312-8",
    abstract = "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."
}