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@article{186176,
        author = {Vijay Kumar S B and Akash Harihar and Vedanth R and Sanjana S V and Ankitha S},
        title = {A Comprehensive Survey of AI-Enabled Techniques for Automated Legal Text Summarization and Citation Grounding in Judicial Applications},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {7654-7667},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186176},
        abstract = {In an age where the sheer volume and complexity of legal texts challenge accessibility, comprehension, and timely justice, artificial intelligence (AI) rises as a transformative ally in the legal domain. This literature survey traverses the evolving landscape of legal text summarization, spotlighting innovations driven by Natural Language Processing (NLP) and Large Language Models (LLMs). From abstractive techniques leveraging T5, BART, and Pegasus to retrieval-augmented systems like RAG and citation-aware frameworks like CitaLaw, the research community is redefining how judgments, statutes, and legal arguments are condensed and understood. Across multilingual jurisdictions, domain-specific challenges such as legal fidelity, citation grounding, and interpretability are met with hybrid models, semantic enrichment, and prototype-based learning. This survey not only maps state-of-the-art methods but also reveals a deeper vision using AI to democratize legal knowledge, streamline case analysis, and bridge the gap between law and layperson. As AI reshapes the contours of legal intelligence, its promise lies not just in automation but in upholding justice, enhancing transparency, and building a future where legal systems are smarter, faster, and more inclusive.},
        keywords = {},
        month = {November},
        }