mlsanityData to 14 Oct 2026

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

arXiv 2508.00476 ↗First posted 1 Aug 2025cs.CLcited by 2

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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).

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

12 of 18 references.

References · 12

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  3. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
  4. 2023Can Large Language Models Be an Alternative to Human Evaluations?cited by 734
  5. 2023Prometheus: Inducing Fine-grained Evaluation Capability in Language Modelscited by 575
  6. 2024LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compressioncited by 348
  7. 2019Document Expansion by Query Predictioncited by 231
  8. 2020Leveraging Abstract Meaning Representation for Knowledge Base Question Answeringcited by 53
  9. 2024Analyzing the Role of Semantic Representations in the Era of Large Language Modelscited by 12
  10. 2024ELITR-Bench: A Meeting Assistant Benchmark for Long-Context Language Modelscited by 9
  11. 2024Efficient Data Generation for Source-grounded Information-seeking Dialogs: A Use Case for Meeting Transcriptscited by 6
  12. 2023MeeQA: Natural Questions in Meeting Transcriptscited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Findings of the Third Automatic Minuting (AutoMin) Challengecited by 1
  2. 2026RAGPerf: An End-to-End Benchmarking Framework for Retrieval-Augmented Generation Systemscited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2025A Systematic Review of Key Retrieval-Augmented Generation (RAG) Systems: Progress, Gaps, and Future Directions
  2. 2025MTRAG: A Multi-Turn Conversational Benchmark for Evaluating Retrieval-Augmented Generation Systems
  3. 2025Structured RAG for Answering Aggregative Questions
  4. 2024Retrieval-Augmented Generation for Domain-Specific Question Answering: A Case Study on Pittsburgh and CMU
  5. 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions
  6. 2024RAGBench: Explainable Benchmark for Retrieval-Augmented Generation Systems
  7. 2025Question Decomposition for Retrieval-Augmented Generation
  8. 2024Know Your RAG: Dataset Taxonomy and Generation Strategies for Evaluating RAG Systems