Multimedia summarization with multimodal output can play an essential role in real-world applications, i.e., automatically generating cover images and titles for news articles or providing introductions to online videos. In this work, we propose a multimodal hierarchical multimedia summarization (MHMS) framework by interacting visual and language domains to generate both video and textual summaries. Our MHMS method contains video and textual segmentation and summarization module, respectively. It formulates a cross-domain alignment objective with optimal transport distance which leverages cross-domain interaction to generate the representative keyframe and textual summary. We evaluated MHMS on three recent multimodal datasets and demonstrated the effectiveness of our method in producing high-quality multimodal summaries.
@article{arxiv.2204.03734,
title = {MHMS: Multimodal Hierarchical Multimedia Summarization},
author = {Jielin Qiu and Jiacheng Zhu and Mengdi Xu and Franck Dernoncourt and Trung Bui and Zhaowen Wang and Bo Li and Ding Zhao and Hailin Jin},
journal= {arXiv preprint arXiv:2204.03734},
year = {2022}
}