Skip to yearly menu bar Skip to main content


MMSum: A Dataset for Multimodal Summarization and Thumbnail Generation of Videos

Jielin Qiu · Jiacheng Zhu · William Han · Aditesh Kumar · Karthik Mittal · Claire Jin · Zhengyuan Yang · Linjie Li · Jianfeng Wang · DING ZHAO · Bo Li · Lijuan Wang

Arch 4A-E Poster #228
award Highlight
[ ]
Fri 21 Jun 10:30 a.m. PDT — noon PDT


Multimodal summarization with multimodal output (MSMO) has emerged as a promising research direction. Nonetheless, numerous limitations exist within existing public MSMO datasets, including insufficient maintenance, data inaccessibility, limited size, and the absence of proper categorization, which pose significant challenges.To address these challenges and provide a comprehensive dataset for this new direction, we have meticulously curated the \textbf{MMSum} dataset. Our new dataset features (1) Human-validated summaries for both video and textual content, providing superior human instruction and labels for multimodal learning.(2) Comprehensively and meticulously arranged categorization, spanning 17 principal categories and 170 subcategories to encapsulate a diverse array of real-world scenarios.(3) Benchmark tests performed on the proposed dataset to assess various tasks and methods, including \textit{video summarization}, \textit{text summarization}, and \textit{multimodal summarization}. To champion accessibility and collaboration, we will release the \textbf{MMSum} dataset and the data collection tool as fully open-source resources, fostering transparency and accelerating future developments.

Live content is unavailable. Log in and register to view live content