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 classification pipeline powered by a fine-tuned Large Language Model (LLM) that categorizes comments into five pragmatic types and aligns them with relevant sentences, and a presentation module that integrates comments inline or as side notes, supported by visual cues like colors, charts, and highlights. Technical evaluation demonstrates that the fine-tuned model effectively captures implicit pragmatic functions and context, achieving solid performance in semantic classification (accuracy=0.89) and position exact match (EM=0.82). A user study (N=12) further demonstrates that the sentence-end embedding improved comment discovery accuracy and reading fluency, while reducing mental demand and perceived effort compared to traditional baselines.

翻译：暂无翻译