What Is That Talk About? A Video-to-Text Summarization Dataset for Scientific Presentations
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866910965843361792 |
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| author | Liu, Dongqi Whitehouse, Chenxi Yu, Xi Mahon, Louis Saxena, Rohit Zhao, Zheng Qiu, Yifu Lapata, Mirella Demberg, Vera |
| author_facet | Liu, Dongqi Whitehouse, Chenxi Yu, Xi Mahon, Louis Saxena, Rohit Zhao, Zheng Qiu, Yifu Lapata, Mirella Demberg, Vera |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_08279 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | What Is That Talk About? A Video-to-Text Summarization Dataset for Scientific Presentations Liu, Dongqi Whitehouse, Chenxi Yu, Xi Mahon, Louis Saxena, Rohit Zhao, Zheng Qiu, Yifu Lapata, Mirella Demberg, Vera Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition 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. |
| title | What Is That Talk About? A Video-to-Text Summarization Dataset for Scientific Presentations |
| topic | Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.08279 |