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.