@inproceedings{sha-etal-2025-integrating,
    title = "Integrating Graph based Algorithm and Transformer Models for Abstractive Summarization",
    author = "Sha, Sayed Ayaan Ahmed  and
      Sivanesan, Sangeetha  and
      Madasamy, Anand Kumar  and
      Binu, Navya",
    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.21/",
    pages = "186--190",
    ISBN = "979-8-89176-312-8",
    abstract = "Summarizing legal documents is a challenging and critical task in the field of Natural Language Processing(NLP). On top of that generating abstractive summaries for legal judgments poses a significant challenge to researchers as there is limitation in the number of input tokens for various language models. In this paper we experimented with two models namely BART base model finetuned on CNN DailyMail dataset along with TextRank and pegasus{\_}indian{\_}legal, a finetuned version of legal-pegasus on Indian legal judgments for generating abstractive summaries for Indian legal documents as part of the JUSTNLP 2025 - Shared Task on Legal Summarization. BART+TextRank outperformed pegasus{\_}indian{\_}legal with a score of 18.84."
}