@inproceedings{choubey-etal-2025-turning,
    title = "Turning Conversations into Workflows: A Framework to Extract and Evaluate Dialog Workflows for Service {AI} Agents",
    author = "Choubey, Prafulla Kumar  and
      Peng, Xiangyu  and
      Bhagavath, Shilpa  and
      Xiong, Caiming  and
      Pentyala, Shiva Kumar  and
      Wu, Chien-Sheng",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.findings-acl.203/",
    pages = "3933--3954",
    ISBN = "979-8-89176-256-5",
    abstract = "Automated service agents require well-structured workflows to deliver consistent and accurate responses to customer queries. However, such workflows are often undocumented, and their automatic extraction from conversations remains largely unexplored. In this work, we present a novel framework for extracting and evaluating dialog workflows from historical interactions. Our extraction process involves two key stages: (1) a retrieval step to select relevant conversations based on key procedural elements, and (2) a structured workflow generation step using question-answer-based chain-of-thought (QA-CoT) prompting. To comprehensively evaluate the quality of the extracted workflows, we introduce an automated simulation framework with agent and customer bots that measures their effectiveness in resolving customer issues. Extensive experiments on the ABCD and SynthABCD datasets show that our QA-CoT technique improves workflow extraction by 12.16{\%} in average macro accuracy over the baseline. Moreover, our evaluation method closely aligns with human assessments, offering a reliable and scalable framework for future research."
}