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

arXiv: https://arxiv.org/abs/2508.00476 · HTML: https://mlsanity.com/paper/2508.00476/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 18 references that are in the map.
- First posted on arXiv 1 Aug 2025.

## Builds on (most cited first, 12 of 12)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Can Large Language Models Be an Alternative to Human Evaluations?](https://mlsanity.com/paper/2305.01937/) (2023)
- [Prometheus: Inducing Fine-grained Evaluation Capability in Language Models](https://mlsanity.com/paper/2310.08491/) (2023)
- [LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression](https://mlsanity.com/paper/2403.12968/) (2024)
- [Document Expansion by Query Prediction](https://mlsanity.com/paper/1904.08375/) (2019)
- [Leveraging Abstract Meaning Representation for Knowledge Base Question Answering](https://mlsanity.com/paper/2012.01707/) (2020)
- [Analyzing the Role of Semantic Representations in the Era of Large Language Models](https://mlsanity.com/paper/2405.01502/) (2024)
- [ELITR-Bench: A Meeting Assistant Benchmark for Long-Context Language Models](https://mlsanity.com/paper/2403.20262/) (2024)
- [Efficient Data Generation for Source-grounded Information-seeking Dialogs: A Use Case for Meeting Transcripts](https://mlsanity.com/paper/2405.01121/) (2024)
- [MeeQA: Natural Questions in Meeting Transcripts](https://mlsanity.com/paper/2305.08502/) (2023)

## Built on by (most cited first, 2 of 2)

- [Findings of the Third Automatic Minuting (AutoMin) Challenge](https://mlsanity.com/paper/2509.13814/) (2025)
- [RAGPerf: An End-to-End Benchmarking Framework for Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2603.10765/) (2026)

## Abstract (arXiv)

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).