@inproceedings{chica-2025-automatic,
    title = "Automatic Legal Judgment Summarization Using Large Language Models: A Case Study for the {JUST}-{NLP} 2025 Shared Task",
    author = "Chica, Santiago",
    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.18/",
    pages = "162--170",
    ISBN = "979-8-89176-312-8",
    abstract = "This paper presents the proposal developed for the JUST-NLP 2025 Shared Task on Legal Summarization, which aims to generate abstractive summaries of Indian court judgments. The work describes the motivation, dataset analysis, related work, and proposed methodology based on Large Language Models (LLMs). We analyze the Indian Legal Summarization (InLSum) dataset, review four relevant articles in the summarization of legal texts, and describe the experimental setup involving GPT-4.1 to evaluate the effectiveness of different prompting strategies. The evaluation will follow the ROUGE and BLEU metrics, consistent with the competition protocol."
}