Who Speaks Matters: Authority-Aware Multi-View RAG over Italian Parliamentary Proceedings

• URL: http://arxiv.org/abs/2608.13410v1
• Date: Thu, 13 Aug 2026 16:07:23 GMT
• Title: Who Speaks Matters: Authority-Aware Multi-View RAG over Italian Parliamentary Proceedings
• Abstract summary: ParliamentRAG is a topic-dependent authority model for parliamentary transcripts.<n>It retrieves relevant speech chunks, identifies topic-relevant experts, and generates a summary synthesizing their perspectives.<n>It is evaluated against Google NotebookLM on 15 policy topics.
• Score: 0.3593955557310285
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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