@inproceedings{d-madasamy-2025-scalar,
    title = "{SC}a{LAR}{\_}{NITK} @ {JUSTNLP} Legal Summarization ({L}-{SUMM}) Shared Task",
    author = "D, Arjun T  and
      Madasamy, Anand Kumar",
    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.12/",
    pages = "121--126",
    ISBN = "979-8-89176-312-8",
    abstract = "This paper presents the systems we submitted to the JUST-NLP 2025 Shared Task on Legal Summarization (L-SUMM). Creating abstractive summaries of lengthy Indian court rulings is challenging due to transformer token limits. To address this problem, we compare three systems built on a fine-tuned Legal Pegasus model. System 1 (Baseline) applies a standard hierarchical framework that chunks long documents using naive token-based segmentation. System 2 (RR-Chunk) improves this approach by using a BERT-BiLSTM model to tag sentences with rhetorical roles (RR) and incorporating these tags (e.g., [Facts]. . . ) to enable structurally informed chunking for hierarchical summarization. System 3 (WRR-Tune) tests whether explicit importance cues help the model by assigning importance scores to each RR using the geometric mean of their distributional presence in judgments and human summaries, and finetuning a separate model on text augmented with these tags (e.g., [Facts, importance score 13.58]). A comparison of the three systems demonstrates the value of progressively adding structural and quantitative importance signals to the model{'}s input."
}