NoTeeline: Supporting Real-Time, Personalized Notetaking with LLM-Enhanced Micronotes
Authors:
Faria Huq,
Abdus Samee,
David Chuan-en Lin,
Xiaodi Alice Tang,
Jeffrey P. Bigham
Abstract:
Taking notes quickly while effectively capturing key information can be challenging, especially when watching videos that present simultaneous visual and auditory streams. Manually taken notes often miss crucial details due to the fast-paced nature of the content, while automatically generated notes fail to incorporate user preferences and discourage active engagement with the content. To address…
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Taking notes quickly while effectively capturing key information can be challenging, especially when watching videos that present simultaneous visual and auditory streams. Manually taken notes often miss crucial details due to the fast-paced nature of the content, while automatically generated notes fail to incorporate user preferences and discourage active engagement with the content. To address this, we propose an interactive system, NoTeeline, for supporting real-time, personalized notetaking. Given micronotes, NoTeeline automatically expands them into full-fledged notes using a Large Language Model (LLM). The generated notes build on the content of micronotes by adding relevant details while maintaining consistency with the user's writing style. In a within-subjects study (n=12), we found that NoTeeline creates high-quality notes that capture the essence of participant micronotes with 93.2% factual correctness and accurately align with participant writing style (8.33% improvement). Using NoTeeline, participants could capture their desired notes with significantly reduced mental effort, writing 47.0% less text and completing their notes in 43.9% less time compared to a manual notetaking baseline. Our results suggest that NoTeeline enables users to integrate LLM assistance in a familiar notetaking workflow while ensuring consistency with their preferences - providing an example of how to address broader challenges in designing AI-assisted tools to augment human capabilities without compromising user autonomy and personalization.
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Submitted 30 January, 2025; v1 submitted 24 September, 2024;
originally announced September 2024.