让议会发言人的权威随话题动态调整，提升多视角信息获取质量。
Who Speaks Matters: Authority-Aware Multi-View RAG over Italian Parliamentary Proceedings
- 根据职业、教育和过往发言构建话题相关权威模型，动态评估发言人可信度。
- 在15个政策主题上实现0.97的政党覆盖度与1.00的引文准确率，优于基准系统。
- 适合关注政治透明度的研究者、记者及公众，特别擅长处理敏感议题的多视角分析。
议会记录是民主讨论的核心资料，但其体量庞大且分散，使公民、记者和研究者难以全面获取多角度信息。将检索增强生成（RAG）应用于议会记录会带来三大风险：高频发言者主导、无法按专业领域加权发言人、政治敏感文本中引用错位。本文提出ParliamentRAG，一个面向意大利众议院的RAG系统，协同解决上述问题。其核心是话题依赖的权威模型，基于职业、教育背景及历史发言，动态估算每位发言人的权威值。用户提问后，系统检索相关发言片段，识别各政团中的主题专家，并生成融合多方观点的摘要，附带支持性引文。在15个政策主题上，通过自动化指标与六位领域专家盲评的双级评估，ParliamentRAG在政党覆盖度（0.97 vs. 0.95）、引文忠实度（1.00 vs. 0.95）等维度优于Google NotebookLM，尤其在来源相关评价上更受青睐；而NotebookLM在语言流畅性方面仍占优。
原文摘要 · Abstract (English)
Parliamentary proceedings are a primary record of democratic deliberation, yet their volume and fragmentation make multi-perspective access difficult for citizens, journalists, and researchers. Applying Retrieval-Augmented Generation (RAG) to parliamentary transcripts introduces three specific risks: dominance of the most frequent speakers, inability to weight speakers according to topical expertise, and citation misattribution in politically sensitive text. We present ParliamentRAG, a RAG system for the Italian Chamber of Deputies that addresses these risks jointly. Its core contribution is a topic-dependent authority model that estimates each speaker's authority as a function of the current query, combining interpretable components such as profession, education, and previous interventions. Given a user query, the system retrieves relevant speech chunks, identifies topic-relevant experts across parliamentary groups, and generates a summary synthesizing their perspectives, accompanied by supporting quotations. ParliamentRAG is evaluated against Google NotebookLM on 15 policy topics via a two-level protocol combining automated metrics and blind A/B human evaluation by six domain experts. The system achieves higher coverage across political groups (0.97 vs. 0.95), perfect quotation faithfulness (1.00 vs. 0.95), and stronger expert preferences on source-related dimensions, while NotebookLM remains stronger on prose-oriented dimensions.
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