Compute to Tell the Tale: Goal-Driven Narrative Generation
Yongkang Wong,Shaojing Fan,Yangyang Guo,Ziwei Xu,Karen Stephen,Rishabh Sheoran,Anusha Bhamidipati,Vivek Barsopia,Jianquan Liu,Mohan S. Kankanhalli +9 more
TLDR
This paper reviews the problem of computational narrative generation where a goal-driven narrative (in the form of text with or without video) is generated from a single or multiple long videos and outlines a general narrative generation framework.Abstract:
Man is by nature a social animal. One important facet of human evolution is through narrative imagination, be it fictional or factual, and to tell the tale to other individuals. The factual narrative, such as news, journalism, field report, etc., is based on real-world events and often requires extensive human efforts to create. In the era of big data where video capture devices are commonly available everywhere, a massive amount of raw videos (including life-logging, dashcam or surveillance footage) are generated daily. As a result, it is rather impossible for humans to digest and analyze these video data. This paper reviews the problem of computational narrative generation where a goal-driven narrative (in the form of text with or without video) is generated from a single or multiple long videos. Importantly, the narrative generation problem makes itself distinguished from the existing literature by its focus on a comprehensive understanding of user goal, narrative structure and open-domain input. We tentatively outline a general narrative generation framework and discuss the potential research problems and challenges in this direction. Informed by the real-world impact of narrative generation, we then illustrate several practical use cases in Video Logging as a Service platform which enables users to get more out of the data through a goal-driven intelligent storytelling AI agent.read more
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