GETALP@AutoMin 2025: Leveraging RAG to Answer Questions based on Meeting Transcripts
This work addresses a domain-specific problem for meeting analysis, presenting an incremental improvement over existing methods.
The paper tackled question-answering from meeting transcripts by combining retrieval augmented generation (RAG) with Abstract Meaning Representations (AMR), resulting in high-quality responses for about 35% of questions and notable improvements for participant-related queries.
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).