VMSMO: Learning to Generate Multimodal Summary for Video-based News Articles

Mingzhe Li, Xiuying Chen, Shen Gao, Zhangming Chan, Dongyan Zhao, Rui Yan


Abstract
A popular multimedia news format nowadays is providing users with a lively video and a corresponding news article, which is employed by influential news media including CNN, BBC, and social media including Twitter and Weibo. In such a case, automatically choosing a proper cover frame of the video and generating an appropriate textual summary of the article can help editors save time, and readers make the decision more effectively. Hence, in this paper, we propose the task of Video-based Multimodal Summarization with Multimodal Output (VMSMO) to tackle such a problem. The main challenge in this task is to jointly model the temporal dependency of video with semantic meaning of article. To this end, we propose a Dual-Interaction-based Multimodal Summarizer (DIMS), consisting of a dual interaction module and multimodal generator. In the dual interaction module, we propose a conditional self-attention mechanism that captures local semantic information within video and a global-attention mechanism that handles the semantic relationship between news text and video from a high level. Extensive experiments conducted on a large-scale real-world VMSMO dataset show that DIMS achieves the state-of-the-art performance in terms of both automatic metrics and human evaluations.
Anthology ID:
2020.emnlp-main.752
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
9360–9369
Language:
URL:
https://aclanthology.org/2020.emnlp-main.752
DOI:
10.18653/v1/2020.emnlp-main.752
Bibkey:
Cite (ACL):
Mingzhe Li, Xiuying Chen, Shen Gao, Zhangming Chan, Dongyan Zhao, and Rui Yan. 2020. VMSMO: Learning to Generate Multimodal Summary for Video-based News Articles. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 9360–9369, Online. Association for Computational Linguistics.
Cite (Informal):
VMSMO: Learning to Generate Multimodal Summary for Video-based News Articles (Li et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.752.pdf
Video:
 https://slideslive.com/38938761
Code
 yingtaomj/VMSMO
Data
VMSMO