Video Timeline Modeling For News Story Understanding

Meng Liu, Mingda Zhang, Jialu Liu, H. Dai, Mingming Yang +3 more
9/1/2023

No official code found. Best implementation: google-research/google-research (39,000 stars, Apache-2.0, last commit 2026-04-30).

Abstract

In this paper, we present a novel problem, namely video timeline modeling. Our objective is to create a video-associated timeline from a set of videos related to a specific topic, thereby facilitating the content and structure understanding of the story being told. This problem has significant potential in various real-world applications, for instance, news story summarization. To bootstrap research in this area, we curate a realistic benchmark dataset, YouTube-News-Timeline, consisting of over $12$k timelines and $300$k YouTube news videos. Additionally, we propose a set of quantitative metrics to comprehensively evaluate and compare methodologies. With such a testbed, we further develop and benchmark several deep learning approaches to tackling this problem. We anticipate that this exploratory work will pave the way for further research in video timeline modeling. The assets are available via https://github.com/google-research/google-research/tree/master/video_timeline_modeling.

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Code Implementations(1)

google-research/google-researchCommunity100%
Apache-2.0

Google Research

39,0008,488C, ROct 4, 20185 months agoApache-2.0
aimachine-learningresearch

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Cite this paper

@article{liu2023video,
  title  = {Video Timeline Modeling For News Story Understanding},
  author = {Meng Liu and Mingda Zhang and Jialu Liu and H. Dai and Mingming Yang and S. Ji and Zheyun Feng and Boqing Gong},
  year   = {2023},
  eprint = {2309.13446},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/pdf/2309.13446},
  journal = {NEURIPS 2023 2023}
}

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