@inproceedings{lucas-etal-2025-chain,
    title = "Chain-of-Interactions: Multi-step Iterative {ICL} Framework for Abstractive Task-Oriented Dialogue Summarization of Conversational {AI} Interactions",
    author = "Lucas, Jason S  and
      Al Lawati, Ali  and
      Nahar, Mahjabin  and
      Chen, John  and
      Mehrabani, Mahnoosh",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.findings-emnlp.191/",
    doi = "10.18653/v1/2025.findings-emnlp.191",
    pages = "3560--3599",
    ISBN = "979-8-89176-335-7",
    abstract = "Large Language Models (LLMs) have introduced paradigm-shifting approaches in natural language processing. Yet, their transformative in-context learning (ICL) capabilities remain underutilized, especially in customer service dialogue summarization{---}a domain plagued by generative hallucinations, detail omission, and inconsistencies. We present Chain-of-Interactions (CoI), a novel single-instance, multi-step framework that orchestrates information extraction, self-correction, and evaluation through sequential interactive generation chains. By strategically leveraging LLMs' ICL capabilities through precisely engineered prompts, CoI dramatically enhances abstractive task-oriented dialogue summarization (ATODS) quality and usefulness. Our comprehensive evaluation on real-world and benchmark human-agent interaction datasets demonstrates CoI{'}s effectiveness through rigorous testing across 11 models and 7 prompting approaches, with 9 standard automatic evaluation metrics, 3 LLM-based evaluations, and human studies involving 480 evaluators across 9 quality dimensions. Results reveal CoI{'}s decisive superiority, outperforming all single-step approaches and achieving 6{\texttimes} better entity preservation, 49{\%} higher quality scores, and 322{\%} improvement in accuracy compared to state-of-the-art multi-step Chain-of-Density (CoD). This research addresses critical gaps in task-oriented dialogue summarization for customer service applications and establishes new standards for harnessing LLMs' reasoning capabilities in practical, industry-relevant contexts."
}