@inproceedings{wang-etal-2025-uncovering,
    title = "Uncovering Argumentative Flow: A Question-Focus Discourse Structuring Framework",
    author = "Wang, Yini  and
      Zhou, Xian  and
      Zheng, Shengan  and
      Huang, Linpeng  and
      Luo, Zhunchen  and
      Luo, Wei  and
      Bai, Xiaoying",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.416/",
    pages = "8252--8270",
    ISBN = "979-8-89176-332-6",
    abstract = "Understanding the underlying argumentative flow in analytic argumentative writing is essential for discourse comprehension, especially in complex argumentative discourse such as think-tank commentary. However, existing structure modeling approaches often rely on surface-level topic segmentation, failing to capture the author{'}s rhetorical intent and reasoning process. To address this limitation, we propose a Question-Focus discourse structuring framework that explicitly models the underlying argumentative flow by anchoring each argumentative unit to a guiding question (reflecting the author{'}s intent) and a set of attentional foci (highlighting analytical pathways). To assess its effectiveness, we introduce an argument reconstruction task in which the modeled discourse structure guides both evidence retrieval and argument generation. We construct a high-quality dataset comprising 600 authoritative Chinese think-tank articles for experimental analysis. To quantitatively evaluate performance, we propose two novel metrics: (1) Claim Coverage, measuring the proportion of original claims preserved or similarly expressed in reconstructions, and (2) Evidence Coverage, assessing the completeness of retrieved supporting evidences. Experimental results show that our framework uncovers the author{'}s argumentative logic more effectively and offers better structural guidance for reconstruction, yielding up to a 10{\%} gain in claim coverage and outperforming strong baselines across both curated and LLM-based metrics."
}