GETALP@AutoMin 2025: Leveraging RAG to Answer Questions based on Meeting Transcripts

• URL: http://arxiv.org/abs/2508.00476v1
• Date: Fri, 01 Aug 2025 09:51:05 GMT
• Title: GETALP@AutoMin 2025: Leveraging RAG to Answer Questions based on Meeting Transcripts
• Authors: Jeongwoo Kang, Markarit Vartampetian, Felix Herron, Yongxin Zhou, Diandra Fabre, Gabriela Gonzalez-Saez,
• Abstract summary: This paper documents GETALP's submission to the Third Run of the Automatic Minuting Shared Task at SIGDial 2025.<n>Our method is based on a retrieval augmented generation (RAG) system and Abstract Meaning Representations (AMR)<n>Our results show that incorporating AMR leads to high-quality responses for approximately 35% of the questions.
• Score: 0.18846515534317265
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper documents GETALP's submission to the Third Run of the Automatic Minuting Shared Task at SIGDial 2025. We participated in Task B: question-answering based on meeting transcripts. Our method is based on a retrieval augmented generation (RAG) system and Abstract Meaning Representations (AMR). We propose three systems combining these two approaches. Our results show that incorporating AMR leads to high-quality responses for approximately 35% of the questions and provides notable improvements in answering questions that involve distinguishing between different participants (e.g., who questions).

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