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：公共咨询会产生大量利益相关方提交的数据，这些数据手动分析几乎不可行。我们提出了一套端到端的基于大语言模型的流水线和交互式仪表盘，用于从监管咨询提交中提取结构化主题，并以欧盟委员会《数字公平法案》公开征集证据作为案例研究进行演示。该系统处理原始PDF附件和网页表单回复，提取主题注释，并将每次提取结果定位到源文本中的逐字引用。应用于4,322份DFA提交后，该流水线生成了15,368条主题注释，并辅以20,951条逐字证据引用。所提出的设计遵循三项原则：逐字定位、完全可追溯性和透明性设计。仪表盘通过五个分析视图展示完整的提取数据集，从数据集级别的主题概览到单个段落细节钻取，每个结果均可追溯至其来源。除了预定义的DFA主题类别外，该流水线还生成了某些利益相关方关注点，例如年龄验证、支付处理器审查和数字所有权，这些是固定分类方法无法捕捉的。该流水线是领域通用的；将其应用于新的咨询只需更新提示词并输入新数据集。实时演示可在 https://dfa-dashboard.thalesbertaglia.com/ 获取。代码和处理后的数据已在 https://github.com/thalesbertaglia/dfa-dashboard 公开。