What Is That Talk About? A Video-to-Text Summarization Dataset for Scientific Presentations
Dongqi Liu, Chenxi Whitehouse, Xi Yu, Louis Mahon, Rohit Saxena, Zheng Zhao, Yifu Qiu, Mirella Lapata
Abstract
Transforming recorded videos into concise and accurate textual summaries is a growing challenge in multimodal learning. This paper introduces VISTA, a dataset specifically designed for video-to-text summarization in scientific domains. VISTA contains 18,599 recorded AI conference presentations paired with their corresponding paper abstracts. We benchmark the performance of state-of-the-art large models and apply a plan-based framework to better capture the structured nature of abstracts. Both human and automated evaluations confirm that explicit planning enhances summary quality and factual consistency. However, a considerable gap remains between models and human performance, highlighting the challenges of our dataset. This study aims to pave the way for future research on scientific video-to-text summarization.
BibTeX
@inproceedings{liu-etal-2025-talk,
title = "What Is That Talk About? A Video-to-Text Summarization Dataset for Scientific Presentations",
author = "Liu, Dongqi and
Whitehouse, Chenxi and
Yu, Xi and
Mahon, Louis and
Saxena, Rohit and
Zhao, Zheng and
Qiu, Yifu and
Lapata, Mirella and
Demberg, Vera",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.310/",
doi = "10.18653/v1/2025.acl-long.310",
pages = "6187--6210",
ISBN = "979-8-89176-251-0"
}