@inproceedings{zhu-etal-2025-mfinmeeting,
    title = "MFinMeeting: A Multilingual, Multi-Sector, and Multi-Task Financial Meeting Understanding Evaluation Dataset",
    author = "Zhu, Jie  and
      Li, Junhui  and
      Wen, Yalong  and
      Li, Xiandong  and
      Guo, Lifan  and
      Chen, Feng",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/transition-to-people-yaml/2025.findings-acl.14/",
    doi = "10.18653/v1/2025.findings-acl.14",
    pages = "244--266",
    ISBN = "979-8-89176-256-5",
    abstract = "Recent breakthroughs in large language models (LLMs) have led to the development of new benchmarks for evaluating their performance in the financial domain. However, current financial benchmarks often rely on news articles, earnings reports, or announcements, making it challenging to capture the real-world dynamics of financial meetings. To address this gap, we propose a novel benchmark called MFinMeeting, which is a multilingual, multi-sector, and multi-task dataset designed for financial meeting understanding. First, MFinMeeting supports English, Chinese, and Japanese, enhancing comprehension of financial discussions in diverse linguistic contexts. Second, it encompasses various industry sectors defined by the Global Industry Classification Standard (GICS), ensuring that the benchmark spans a broad range of financial activities. Finally, MFinMeeting includes three tasks: summarization, question-answer (QA) pair extraction, and question answering, facilitating a more realistic and comprehensive evaluation of understanding. Experimental results with seven popular LLMs reveal that even the most advanced long-context models have significant room for improvement, demonstrating the effectiveness of MFinMeeting as a benchmark for assessing LLMs' financial meeting comprehension skills."
}