GETALP@AutoMin 2025: Leveraging RAG to Answer Questions based on Meeting Transcripts
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915421753442304 |
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| author | Kang, Jeongwoo Vartampetian, Markarit Herron, Felix Zhou, Yongxin Fabre, Diandra Gonzalez-Saez, Gabriela |
| author_facet | Kang, Jeongwoo Vartampetian, Markarit Herron, Felix Zhou, Yongxin Fabre, Diandra Gonzalez-Saez, Gabriela |
| contents | 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). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_00476 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GETALP@AutoMin 2025: Leveraging RAG to Answer Questions based on Meeting Transcripts Kang, Jeongwoo Vartampetian, Markarit Herron, Felix Zhou, Yongxin Fabre, Diandra Gonzalez-Saez, Gabriela Computation and Language 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). |
| title | GETALP@AutoMin 2025: Leveraging RAG to Answer Questions based on Meeting Transcripts |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2508.00476 |