ChunkyEdit: Text-first video interview editing via chunking

Document Title

ChunkyEdit: Text-first video interview editing via chunking

Document Information

• Subject Area: Human-Computer Interaction and Video Editing Tool Design
• Keywords: Video editing, chunking, topic modeling, text-driven video editing, user interface design, human-computer interaction, natural language processing, time efficiency, creative control

Research Background and Problem

• Identified Problems or Challenges: The video editing process, especially in its early stages, requires editors to handle large amounts of video material and filter key content based on themes. This process is often manual and time-consuming. In existing tools, editors need to repeatedly watch, mark, and organize clips, which is not only tedious but also prone to cognitive overload.

• Why It Matters: Editors need to organize large amounts of material and create logical content, but human working memory capacity is limited. Using a "chunking" approach to assist editors in video management can improve efficiency and allow them to focus more on storytelling and narrative decision-making.

• Research Motivation and Related Work: While some tools currently utilize text-to-speech technology or topic modeling techniques to support editing, many tools are either overly automated (resulting in a lack of flexibility and creative control) or require editors to perform cumbersome manual operations. ChunkyEdit aims to strike a balance—automatically organizing material through "chunking" techniques while preserving editors' control over key narrative decisions.

Solution

• Proposed Solution: ChunkyEdit is a text-based early-stage video editing tool focused on "chunking" video content. It uses themes or questions extracted from video transcripts to automatically segment videos into logical chunks, while allowing users to adjust, expand, and validate these chunks.

• Innovations:

  1. Automatically groups text transcripts into theme-based "chunk" units.
  2. Bridges the gap between full automation and manual editing by offering "semi-automated" video organization.
  3. Combines multiple topic modeling techniques (e.g., GPT-4 and keyword extraction) for chunk theme identification, making the tool widely applicable.
  4. Provides different chunking methods tailored to specific project types (e.g., question-based or theme-based chunking).
  5. Allows exporting intermediate results (e.g., text-based "paper edits" or preliminary video timelines) compatible with other editing tools.

• Implementation Steps and Techniques:

  1. Video Transcription and Preprocessing: Uses Speechmatics to obtain accurate word-by-word transcripts, including timestamps and speaker identification.
  2. Chunking Strategies:
     • Question-based chunking: Groups similar or follow-up questions together.
     • Answer theme-based chunking: Uses GPT-4 or embedding models for thematic analysis.
  3. User Interface Design: Features an editing panel and review panel, enabling users to intuitively manage chunks through interactive operations.
  4. Export Support: Generates "paper edits" (PDF format) or exports EDL files compatible with mainstream video editing tools.
  5. User Engagement Optimization: Allows editors to add placeholder video clips (B-roll) or restructure content.

Research Outcomes

• Specific Results:

  1. ChunkyEdit can automatically chunk video interview content ranging from 4 to 81 minutes, tested on 12 video datasets.
  2. Initial results indicate that the tool generates easily editable thematic chunks aligned with editors' workflows.
  3. User evaluations show that ChunkyEdit helps identify thematic chunks more efficiently than manual marking.

• Advantages Over Existing Solutions:

  1. Rapid classification and filtering of video material, saving time on manual marking and organization.
  2. Better meets editors' needs for creative control compared to fully automated editing.
  3. Intuitive user interface design is easy to adopt and seamlessly integrates into existing editing toolchains.

• Experiment or Evaluation Results:

  • Eight professional video editors participated in the evaluation.
  • On average, users found ChunkyEdit particularly suitable for large video projects requiring deep organization, with potential to accelerate early-stage editing processes.
  • Among different chunking methods, GPT-4-based thematic chunking (Answer 1 method) received the highest user approval.
  • Editors rated the tool highly (7-10 points), noting that more time was saved for creative thinking and high-level decision-making rather than repetitive operations.

• Limitations and Future Directions:

  1. Currently, the tool can only chunk single video interviews. Expanding functionality to manage complex multi-video archives and cross-video thematic associations is needed.
  2. ChunkyEdit's granularity is fixed at "question-answer pairs," which may require further exploration for finer granularity in long responses or non-conversational video editing.
  3. Further optimization of topic modeling and chunk count generation parameters is needed to match diverse user preferences.
  4. Plans to extend the technology to support multi-language corpora chunking and broader video types (e.g., non-dialogue video segments).

In summary, this study proposes an efficient video chunking editing system that combines topic modeling and semi-automated workflow design, providing strong support for interview-style video editing while preserving creators' flexibility. This offers valuable research paths for innovative video editing tool development and human-computer interaction design.