@inproceedings{gupta-etal-2025-autosumm,
    title = "{AUTOSUMM}: A Comprehensive Framework for {LLM}-Based Conversation Summarization",
    author = "Gupta, Abhinav  and
      Singh, Devendra  and
      Cowan, Greig A  and
      Kadhiresan, N  and
      Srivastava, Siddharth  and
      Sriraja, Yagneswaran  and
      Mantri, Yoages Kumar",
    editor = "Rehm, Georg  and
      Li, Yunyao",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-industry.35/",
    pages = "500--509",
    ISBN = "979-8-89176-288-6",
    abstract = "We present AUTOSUMM, a large language model (LLM)-based summarization system deployed in a regulated banking environment to generate accurate, privacy-compliant summaries of customer-advisor conversations. The system addresses challenges unique to this domain, including speaker attribution errors, hallucination risks, and short or low-information transcripts. Our architecture integrates dynamic transcript segmentation, thematic coverage tracking, and a domain specific multi-layered hallucination detection module that combines syntactic, semantic, and entailment-based checks. Human-in-the-loop feedback from over 300 advisors supports continuous refinement and auditability.Empirically, AUTOSUMM achieves a 94{\%} factual consistency rate and a significant reduction in hallucination rate. In production, 89{\%} of summaries required no edits, and only 1{\%} required major corrections. A structured model version management pipeline ensures stable upgrades with minimal disruption. We detail our deployment methodology, monitoring strategy, and ethical safeguards, showing how LLMs can be reliably integrated into high-stakes, regulated workflows."
}