A Dataset for Telling the Stories of Social Media Videos

Spandana Gella, Mike Lewis, Marcus Rohrbach


Abstract
Video content on social media platforms constitutes a major part of the communication between people, as it allows everyone to share their stories. However, if someone is unable to consume video, either due to a disability or network bandwidth, this severely limits their participation and communication. Automatically telling the stories using multi-sentence descriptions of videos would allow bridging this gap. To learn and evaluate such models, we introduce VideoStory a new large-scale dataset for video description as a new challenge for multi-sentence video description. Our VideoStory captions dataset is complementary to prior work and contains 20k videos posted publicly on a social media platform amounting to 396 hours of video with 123k sentences, temporally aligned to the video.
Anthology ID:
D18-1117
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
968–974
Language:
URL:
https://aclanthology.org/D18-1117
DOI:
10.18653/v1/D18-1117
Bibkey:
Cite (ACL):
Spandana Gella, Mike Lewis, and Marcus Rohrbach. 2018. A Dataset for Telling the Stories of Social Media Videos. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 968–974, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
A Dataset for Telling the Stories of Social Media Videos (Gella et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1117.pdf
Video:
 https://aclanthology.org/D18-1117.mp4
Data
ActivityNet CaptionsYouCook