What is it about?
This study presents an AI-powered system designed to help teams manage comments more efficiently in collaborative tools like Google Docs. The system automatically classifies and ranks comments based on urgency, importance, sentiment, and other factors to ensure that the most critical feedback gets addressed first. It uses models such as BERT, RoBERTa, and GEMMA-2B for classification, and applies topic modeling to identify recurring themes. This approach makes it easier to navigate high volumes of feedback and improves teamwork in shared documents.
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Why is it important?
As collaborative work becomes more common, the volume of comments in shared documents can slow productivity and cause important feedback to be overlooked. This work is timely because it introduces a scalable and adaptable AI triage system that prioritizes comments based on their urgency and relevance. It shows how integrating large language models and topic analysis can enhance document collaboration, particularly in fast-paced or high-stakes environments like academic writing, project management, or software documentation.
Perspectives
From my perspective, this work bridges a practical gap in collaboration tools. While comment features exist in many platforms, users often lack a way to filter or prioritize them efficiently. Building and testing this triage system allowed me to see how AI can directly support human workflows by reducing decision fatigue and improving communication flow. It also showed the importance of balancing technical model performance with real-world usability, especially when dealing with sparse or evolving data categories
Vamsi Krishna Pasam
Clemson University
Read the Original
This page is a summary of: AI-Powered Comment Triage for Efficient Collaboration and Feedback Management, March 2025, ACM (Association for Computing Machinery),
DOI: 10.1145/3672608.3707835.
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