@inproceedings{liu-etal-2025-mdseval,
title = "{MDSE}val: A Meta-Evaluation Benchmark for Multimodal Dialogue Summarization",
author = "Liu, Yinhong and
He, Jianfeng and
Su, Hang and
Lian, Ruixue and
Nian, Yi and
Vincent, Jake W. and
Vishnubhotla, Srikanth and
Piramuthu, Robinson and
Mansour, Saab",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.794/",
pages = "14707--14727",
ISBN = "979-8-89176-335-7",
abstract = "Multimodal Dialogue Summarization (MDS) is a critical task with wide-ranging applications. To support the development of effective MDS models, robust automatic evaluation methods are essential for reducing both cost and human effort. However, such methods require a strong meta-evaluation benchmark grounded in human annotations. In this work, we introduce MDSEval, the first meta-evaluation benchmark for MDS, consisting image-sharing dialogues, corresponding summaries, and human judgments across eight well-defined quality aspects. To ensure data quality and richfulness, we propose a novel filtering framework leveraging Mutually Exclusive Key Information (MEKI) across modalities. Our work is the first to identify and formalize key evaluation dimensions specific to MDS. Finally, we benchmark state-of-the-art modal evaluation methods, revealing their limitations in distinguishing summaries from advanced MLLMs and their susceptibility to various bias."
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<abstract>Multimodal Dialogue Summarization (MDS) is a critical task with wide-ranging applications. To support the development of effective MDS models, robust automatic evaluation methods are essential for reducing both cost and human effort. However, such methods require a strong meta-evaluation benchmark grounded in human annotations. In this work, we introduce MDSEval, the first meta-evaluation benchmark for MDS, consisting image-sharing dialogues, corresponding summaries, and human judgments across eight well-defined quality aspects. To ensure data quality and richfulness, we propose a novel filtering framework leveraging Mutually Exclusive Key Information (MEKI) across modalities. Our work is the first to identify and formalize key evaluation dimensions specific to MDS. Finally, we benchmark state-of-the-art modal evaluation methods, revealing their limitations in distinguishing summaries from advanced MLLMs and their susceptibility to various bias.</abstract>
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%0 Conference Proceedings
%T MDSEval: A Meta-Evaluation Benchmark for Multimodal Dialogue Summarization
%A Liu, Yinhong
%A He, Jianfeng
%A Su, Hang
%A Lian, Ruixue
%A Nian, Yi
%A Vincent, Jake W.
%A Vishnubhotla, Srikanth
%A Piramuthu, Robinson
%A Mansour, Saab
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F liu-etal-2025-mdseval
%X Multimodal Dialogue Summarization (MDS) is a critical task with wide-ranging applications. To support the development of effective MDS models, robust automatic evaluation methods are essential for reducing both cost and human effort. However, such methods require a strong meta-evaluation benchmark grounded in human annotations. In this work, we introduce MDSEval, the first meta-evaluation benchmark for MDS, consisting image-sharing dialogues, corresponding summaries, and human judgments across eight well-defined quality aspects. To ensure data quality and richfulness, we propose a novel filtering framework leveraging Mutually Exclusive Key Information (MEKI) across modalities. Our work is the first to identify and formalize key evaluation dimensions specific to MDS. Finally, we benchmark state-of-the-art modal evaluation methods, revealing their limitations in distinguishing summaries from advanced MLLMs and their susceptibility to various bias.
%U https://aclanthology.org/2025.findings-emnlp.794/
%P 14707-14727
Markdown (Informal)
[MDSEval: A Meta-Evaluation Benchmark for Multimodal Dialogue Summarization](https://aclanthology.org/2025.findings-emnlp.794/) (Liu et al., Findings 2025)
ACL
- Yinhong Liu, Jianfeng He, Hang Su, Ruixue Lian, Yi Nian, Jake W. Vincent, Srikanth Vishnubhotla, Robinson Piramuthu, and Saab Mansour. 2025. MDSEval: A Meta-Evaluation Benchmark for Multimodal Dialogue Summarization. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 14707–14727, Suzhou, China. Association for Computational Linguistics.