NoTeeline: Supporting Real-Time Notetaking from Keypoints with Large Language Models
Abstract
Video has become a popular media form for information sharing and consumption. However, taking notes while watching a video requires significant time and effort. To address this, we propose a novel interactive system, NoTeeline, for taking real-time, personalized notes. NoTeeline lets users quickly jot down keypoints (micronotes), which are automatically expanded into full-fledged notes that capture the content of the user's micronotes and are consistent with the user's writing style. In a within-subjects study (N=12), we found that NoTeeline helps users create high-quality notes that capture the essence of their micronotes with a higher factual correctness (93.2%) while accurately reflecting their writing style. While using NoTeeline, participants experienced significantly reduced mental effort, captured satisfactory notes while writing 47% less text, and completed notetaking with 43.9% less time compared to a manual notetaking baseline.
Community
NoTeeline: Supporting Real-Time Notetaking from Keypoints with Large Language Models
Authors: Faria Huq, Abdus Samee, David Chuan-en Lin, Xiaodi Alice Tang, Jeffrey P. Bigham
This is an automated message from the Librarian Bot. I found the following papers similar to this paper.
The following papers were recommended by the Semantic Scholar API
- In-Context Ensemble Improves Video-Language Models for Low-Level Workflow Understanding from Human Demonstrations (2024)
- VideoLLaMB: Long-context Video Understanding with Recurrent Memory Bridges (2024)
- TC-LLaVA: Rethinking the Transfer from Image to Video Understanding with Temporal Considerations (2024)
- Personalized Video Summarization using Text-Based Queries and Conditional Modeling (2024)
- VideoLLM-MoD: Efficient Video-Language Streaming with Mixture-of-Depths Vision Computation (2024)
Please give a thumbs up to this comment if you found it helpful!
If you want recommendations for any Paper on Hugging Face checkout this Space
You can directly ask Librarian Bot for paper recommendations by tagging it in a comment:
@librarian-bot
recommend
Models citing this paper 0
No model linking this paper
Datasets citing this paper 0
No dataset linking this paper
Spaces citing this paper 0
No Space linking this paper