ChatCut: LLMs for Transcript-Driven Editing
ChatCut: LLMs for Transcript-Driven Editing
The modern content space is increasingly driven by verbal communication. From documentaries and journalism to marketing videos and podcasts, spoken dialogue often forms the skeletal foundation of compelling narratives.
The Problem
Despite this centrality of spoken content, creators face persistent challenges in editing hours of verbal footage efficiently.
Current Solutions
Recent innovations have introduced transcript-based video editing tools that represent spoken content as searchable text.
Evolution
To genuinely change this workflow, editing systems must evolve beyond mere transcript indexing toward intelligent, interactive tools that collaborate with users to construct narratives.
Research Question
How can we design an editing tool that fully supports creators in rapidly identifying, selecting, and assembling soundbites into cohesive narratives, without compromising their creative agency?
Our Solution
Building upon recent advances in natural language processing and large language models (LLMs), we introduce ChatCut, a transcript-driven video editing system that allows creators to perform complex editing tasks through conversational language commands.
Conclusion
We introduced ChatCut, a documentary video editing tool that enables a novel transcript-driven editing paradigm through dual-mode AI assistance and transcript-synchronized timeline integration.
For more on transcript-based editing in practice, read our paper edit masterclass with Kaiwen Li, see how YouTubers use ChatCut to edit faster, and explore the transcription tool we always wished existed.