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：长文本在社交平台上普遍存在，但读者常面临信息过载问题，难以定位关键内容。评论为理解、质疑和补充文本提供了宝贵的外部视角，但无序且结构化的呈现方式限制了其潜力。目前鲜有研究探索将评论直接嵌入阅读流程。作为探索性尝试，我们提出CommentScope系统，包含两个核心模块：基于微调大语言模型的分类流水线（可评论文本划分为五种语用类型并与相关句子对齐），以及将评论以内联或旁注形式嵌入的呈现模块，辅以颜色、图表和高亮等视觉线索。技术评估表明，微调模型能有效捕捉隐式语用功能与语境，在语义分类（准确率=0.89）和位置精确匹配（EM=0.82）方面表现优异。用户研究（N=12）进一步证明，与传统基线相比，句尾嵌入方式不仅提升了评论发现准确性和阅读流畅度，还减少了心智负担和感知努力。