# Findings of the Third Automatic Minuting (AutoMin) Challenge

arXiv: https://arxiv.org/abs/2509.13814 · HTML: https://mlsanity.com/paper/2509.13814/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 17 Sep 2025.

## Builds on (most cited first, 6 of 6)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [BARTScore: Evaluating Generated Text as Text Generation](https://mlsanity.com/paper/2106.11520/) (2021)
- [To Ship or Not to Ship: An Extensive Evaluation of Automatic Metrics for Machine Translation](https://mlsanity.com/paper/2107.10821/) (2021)
- [ELITR-Bench: A Meeting Assistant Benchmark for Long-Context Language Models](https://mlsanity.com/paper/2403.20262/) (2024)
- [GETALP@AutoMin 2025: Leveraging RAG to Answer Questions based on Meeting Transcripts](https://mlsanity.com/paper/2508.00476/) (2025)
- [Reducing Hallucinations in Summarization via Reinforcement Learning with Entity Hallucination Index](https://mlsanity.com/paper/2507.22744/) (2025)

## Built on by (most cited first, 1 of 1)

- [Token Distribution versus Data Volume: Domain Balancing in Multi-Domain Meeting Summarisation](https://mlsanity.com/paper/2608.15935/) (2026)

## Abstract (arXiv)

This paper presents the third edition of AutoMin, a shared task on automatic meeting summarization into minutes. In 2025, AutoMin featured the main task of minuting, the creation of structured meeting minutes, as well as a new task: question answering (QA) based on meeting transcripts. The minuting task covered two languages, English and Czech, and two domains: project meetings and European Parliament sessions. The QA task focused solely on project meetings and was available in two settings: monolingual QA in English, and cross-lingual QA, where questions were asked and answered in Czech based on English meetings. Participation in 2025 was more limited compared to previous years, with only one team joining the minuting task and two teams participating in QA. However, as organizers, we included multiple baseline systems to enable a comprehensive evaluation of current (2025) large language models (LLMs) on both tasks.