{"ID":2887890,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.00476","arxiv_id":"2508.00476","title":"GETALP@AutoMin 2025: Leveraging RAG to Answer Questions based on Meeting Transcripts","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).","short_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 s...","url_abs":"https://arxiv.org/abs/2508.00476","url_pdf":"https://arxiv.org/pdf/2508.00476v1","authors":"[\"Jeongwoo Kang\",\"Markarit Vartampetian\",\"Felix Herron\",\"Yongxin Zhou\",\"Diandra Fabre\",\"Gabriela Gonzalez-Saez\"]","published":"2025-08-01T09:51:05Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[]","has_code":false}