A Full-Scale Dataset for Multi-modal Summarization
MM-AVS is a full-scale multimodal dataset comprehensively gathering documents, summaries, images, captions, videos, audios, transcripts, and titles in English from CNN and Daily Mail. To our best knowledge, this is the first collection that spans all modalities and nearly comprises all types of materials available in this community.
We're sharing them here for developers and researchers to explore, study, and learn from.
Given the dataset is quite large, please download the demo firstly to make sure the dataset meets your requirement.
Download files include cnn.zip, dailymail.zip, train_id.txt and test_id.txt
Method1: FTP
ftp://45.77.122.178/pub/file_name (i.e., file_name->cnn.zip)
invalid: this server is not rented anymore in our lab
Method2: Baidu Yun
link：https://pan.baidu.com/s/1I7b18ddgvVvPmb3okaRzpQ
password：uwbt
invalid: Baidu forbid
Method3: OneDrive
link: https://mailnankaieducn-my.sharepoint.com/:f:/g/personal/fuxiyan_mail_nankai_edu_cn/Ep7NuWj9V9FLpI8weBt1O4oBw5sAHNSzaZptJSxmLa4g1g?e=6Ea3Po
Note: Videos are always limited by network disk, we recommend to method1.
The data scale is determined by its accompanied videos, considering this modality is more space-consuming than the other. The data acquirability code can be used for dataset extension.
New papar is accepted as a short paper by NAACL2021.
@inproceedings{fu-etal-2021-mm,
title = "{MM}-{AVS}: A Full-Scale Dataset for Multi-modal Summarization",
author = "Fu, Xiyan and
Wang, Jun and
Yang, Zhenglu",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.473",
doi = "10.18653/v1/2021.naacl-main.473",
pages = "5922--5926",
}
The original version can be found at "Multi-modal Summarization for Video-containing Documents".