@inproceedings{neelamegam-nirmala-2025-contextors,
    title = "Contextors at {L}-{SUMM}: Retriever-Driven Multi-Generator Summarization",
    author = "Neelamegam, Pavithra  and
      Nirmala, S Jaya",
    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.10/",
    pages = "107--112",
    ISBN = "979-8-89176-312-8",
    abstract = "Indian court judgments are very difficult to automatically summarize because of their length, complex legal reasoning and scattered important information. This paper outlines the methodology used for the Legal Summarization (L-SUMM) shared task at the JUST-NLP 2025 Workshop, which aims to provide abstractive summaries of roughly 500 words from english language Indian court rulings that are logical, concise and factually accurate. The paper proposes a Retriever-Driven Multi-Generator Summarization framework that combines a semantic retriever with fine-tuned encoder{--}decoder models BART, Pegasus and LED to enhance legal document summarization. This pipeline uses cosine similarity analysis to improve summary faithfulness, cross-model validation to guarantee factual consistency and iterative retrieval expansion to choose relevant text chunks in order to address document length and reduce hallucinations. Despite being limited to 400{--}500 words, the generated summaries successfully convey legal reasoning. Our team Contextors achieved an average score of 22.51, ranking 4th out of 9 in the L-SUMM shared task leaderboard, demonstrating the efficacy of Retriever-Driven Multi-Generator Summarization approach, which improves transparency, accessibility, and effective understanding of legal documents. This method shows excellent content coverage and coherence when assessed using ROUGE-2, ROUGE-L, and BLEU criteria."
}