ReelFramer: Human-AI Co-Creation for News-to-Video Translation

要旨

Short videos on social media are the dominant way young people consume content. News outlets aim to reach audiences through news reels---short videos conveying news---but struggle to translate traditional journalistic formats into short, entertaining videos. To translate news into social media reels, we support journalists in reframing the narrative. In literature, narrative framing is a high-level structure that shapes the overall presentation of a story. We identified three narrative framings for reels that adapt social media norms but preserve news value, each with a different balance of information and entertainment. We introduce ReelFramer, a human-AI co-creative system that helps journalists translate print articles into scripts and storyboards. ReelFramer supports exploring multiple narrative framings to find one appropriate to the story. AI suggests foundational narrative details, including characters, plot, setting, and key information. ReelFramer also supports visual framing; AI suggests character and visual detail designs before generating a full storyboard. Our studies show that narrative framing introduces the necessary diversity to translate various articles into reels, and establishing foundational details helps generate scripts that are more relevant and coherent. We also discuss the benefits of using narrative framing and foundational details in content retargeting.

著者
Sitong Wang
Columbia University, New York, New York, United States
Samia Menon
Columbia University, New York, New York, United States
Tao Long
Columbia University, New York, New York, United States
Keren Henderson
Syracuse University, Syracuse, New York, United States
Dingzeyu Li
Adobe Research, Seattle, Washington, United States
Kevin Crowston
Syracuse University, Syracuse, New York, United States
Mark Hansen
Columbia University, New York, New York, United States
Jeffrey V. Nickerson
Stevens Institute of Technology, Hoboken, New Jersey, United States
Lydia B. Chilton
Columbia University, New York, New York, United States
論文URL

https://doi.org/10.1145/3613904.3642868

動画

会議: CHI 2024

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2024.acm.org/)

セッション: Creative Professionals and AI A

315
5 件の発表
2024-05-14 23:00:00
2024-05-15 00:20:00