@inproceedings{parada-etal-2025-combining,
    title = "Combining Extractive and Generative Methods for Legal Summarization: {BLANCKED} at {JUST}-{NLP} 2025",
    author = "Parada, Erich Giusseppe Soto  and
      Almeida, Carlos Manuel Mu{\~n}oz  and
      Alba, David Cuevas",
    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.17/",
    pages = "155--161",
    ISBN = "979-8-89176-312-8",
    abstract = "This paper presents Tayronas Trigrams{'}s methodology and findings from our participation in the JUST-NLP 2025 Shared Task of Legal Summarization (L-SUMM), which focused on generating abstractive summaries of lengthy Indian court judgments. Our initial approach involved evaluating and fine-tuning specialized sequence-to-sequence models like Legal-Pegasus, Indian Legal LED, and BART. We found that these small generative models, even after fine-tuning on the limited InLSum dataset (1,200 training examples), delivered performance (e.g., Legal-Pegasus AVG score: 16.50) significantly below expected.Consequently, our final, best-performing method was a hybrid extractive-abstractive pipeline. This approach first employed the extractive method PACSUM to select the most important sentences yielding an initial AVG score of 20.04 and then utilized a Large Language Model (specifically Gemini 2.5 Pro), correctly prompted, to perform the final abstractive step by seamlessly stitching and ensuring coherence between these extracted chunks. This hybrid strategy achieved an average ROUGE-2 of 21.05, ROUGE-L of 24.35, and BLEU of 15.12, securing 7th place in the competition. Our key finding is that, under data scarcity, a two-stage hybrid approach dramatically outperforms end-to-end abstractive fine-tuning on smaller models."
}