Public consultations generate large volumes of data in the form of stakeholder submissions that are practically unfeasible to analyse manually. We present an end-to-end LLM-based pipeline and interactive dashboard for structured topic extraction from regulatory consultation submissions, demonstrated on the European Commission's Digital Fairness Act (DFA) public call for evidence as a case study. The system processes raw PDF attachments and web-form responses, extracts topic annotations, and grounds every extraction in a verbatim quote from the source text. Applied to 4,322 DFA submissions, the pipeline produced 15,368 topic annotations supported by 20,951 verbatim evidence quotes. Three principles govern the proposed design: verbatim grounding, full traceability, and transparency by design. The dashboard exposes the full extraction dataset through five analytical views, from dataset-level topic overviews to individual paragraph drill-downs, with every result traceable to its source. Beyond the predefined DFA topic categories, the pipeline generated certain stakeholder concerns, such as Age Verification, Payment Processor Censorship, and Digital Ownership, that a fixed-taxonomy approach would have missed. The pipeline is domain-generic; adapting it to a new consultation requires only a prompt update and a new dataset. A live demo is available at https://dfa-dashboard.thalesbertaglia.com/. The code and processed data are publicly available at https://github.com/thalesbertaglia/dfa-dashboard.

翻译：公开磋商会生成大量利益相关者提交的数据，人工分析这些数据几乎不可行。我们提出了一种基于大语言模型的端到端流水线与交互式仪表板，用于从监管磋商提交材料中结构化提取主题，并以欧盟委员会《数字公平法案（DFA）》公开征询意见作为案例进行演示。该系统处理原始PDF附件和网络表单回复，提取主题注释，并将每次提取结果锚定至源文本中的逐字引用。应用于4322份DFA提交材料后，该流水线生成了15368条主题注释，并配以20951条逐字证据引用。所提出的设计遵循三项原则：逐字引用锚定、完全可追溯以及透明性设计。该仪表板通过五种分析视图展示完整提取数据集，涵盖从数据集层面的主题概览到单个段落的逐层深入分析，所有结果均可追溯至其原始来源。除预定义的DFA主题类别外，该流水线还识别出固定分类方法可能遗漏的特定利益相关者关注点，如年龄验证、支付处理器审查及数字所有权。该流水线具有领域通用性；将其适配至新磋商场景仅需更新提示词及新数据集。实时演示可在https://dfa-dashboard.thalesbertaglia.com/获取。代码与处理后的数据已公开于https://github.com/thalesbertaglia/dfa-dashboard。