CommentScope: A Comment-Embedded Assisted Reading System for a Long Text

Abstract: Long texts are ubiquitous on social platforms, yet readers often face information overload and struggle to locate key content. Comments provide valuable external perspectives for understanding, questioning, and complementing the text, but their potential is hindered by disorganized and unstructured presentation. Few studies have explored embedding comments directly into reading. As an exploratory step, we propose CommentScope, a system with two core modules: a pipeline that classifies comments into five types and aligns them with relevant sentences, and a presentation module that integrates comments inline or as side notes, supported by visual cues such as colors, charts, and highlights. Technical evaluation shows that the hybrid "Rule+LLM" pipeline achieved solid performance in semantic classification (accuracy=0.90) and position alignment (accuracy=0.88). A user study (N=12) further demonstrated that the sentence-end embedding significantly improved comment discovery accuracy and reading fluency while reducing mental demand and perceived effort.