Short videos on social media are a prime way many young people find and consume content. News outlets would like to reach audiences through news reels, but currently struggle to translate traditional journalistic formats into the short, entertaining videos that match the style of the platform. There are many ways to frame a reel-style narrative around a news story, and selecting one is a challenge. Different news stories call for different framings, and require a different trade-off between entertainment and information. We present a system called ReelFramer that uses text and image generation to help journalists explore multiple narrative framings for a story, then generate scripts, character boards and storyboards they can edit and iterate on. A user study of five graduate students in journalism-related fields found the system greatly eased the burden of transforming a written story into a reel, and that exploring framings to find the right one was a rewarding process.

研究表明，通过使用文本和图像生成技术，ReelFramer系统可以帮助新闻人士探索多种新闻故事的叙述框架，并生成脚本、角色板和故事板，以缓解将书面故事转化为短视频新闻的负担。

利用生成式AI在社交媒体上共同创造新闻短片