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Video Timeline Modeling For News Story Understanding
Meng Liu · Mingda Zhang · Jialu Liu · Hanjun Dai · Ming-Hsuan Yang · Shuiwang Ji · Zheyun Feng · Boqing Gong

Thu Dec 14 08:45 AM -- 10:45 AM (PST) @ Great Hall & Hall B1+B2 #333
Event URL: https://github.com/google-research/google-research/tree/master/video_timeline_modeling »
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.

Author Information

Meng Liu (Texas A&M University)

I am currently a 2nd year Ph.D. student in Department of Computer Science & Engineering, Texas A&M University. My advisor is Dr. Shuiwang Ji, who leads the Data Integration, Visualization, and Exploration (DIVE) Laboratory. I obtained my bachelor’s degree from Department of Electronic Engineering, Tsinghua University in 2019, advised by Prof. Liangrui Peng. My research interests lie in machine learning and data mining.

Mingda Zhang (Google Research)
Jialu Liu (Google)
Hanjun Dai (Google DeepMind)
Ming-Hsuan Yang (Google / UC Merced)
Shuiwang Ji (Texas A&M University)
Zheyun Feng (Google Inc.)
Boqing Gong (Tencent AI Lab)

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