發言者至關重要：義大利議會議事紀錄上的權威感知多視角檢索增強生成

摘要

議會程序是民主審議的主要記錄，然而其數量龐大且內容零散，使得公民、記者與研究人員難以進行多視角取用。將檢索增強生成（RAG）應用於議會逐字紀錄會引入三項具體風險：最頻繁發言者的主導、無法根據主題專業知識對發言者加權，以及在政治敏感文本中的引用誤歸。我們提出 ParliamentRAG，一個針對義大利眾議院的 RAG 系統，共同應對這些風險。其核心貢獻在於一個依賴主題的權威模型，該模型根據當前查詢來估算每位發言者的權威性，並結合職業、教育背景及先前發言等可解釋的組成要素。給定使用者查詢，系統會檢索相關的發言片段，識別各議會黨團中與主題相關的專家，並生成綜合其觀點的摘要，同時附上支持性引文。ParliamentRAG 透過結合自動化指標及六位領域專家進行的盲測 A/B 人工評估之兩級協議，在 15 個政策主題上與 Google NotebookLM 進行比較評估。該系統在跨政治團體的覆蓋率（0.97 對 0.95）、完美的引文忠實度（1.00 對 0.95）以及專家對來源相關維度的偏好上表現更佳，而 NotebookLM 在散文導向維度上仍具優勢。

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.