发表机构
Cornell University; Aarhus University（康奈尔大学; 奥胡斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究构建小说摘要与原作章节映射数据集，测量摘要线性性与一致性，明确文学作品与摘要的情节表达差异。
AI 中文摘要
文学作品结构复杂，需平衡情节、悬念、惊喜与艺术表达；文学摘要侧重情节，可能与原作存在偏差。结合人工与大语言模型（LLM）标注，构建了包含150部小说摘要句子与对应原作章节映射的数据集，发现该任务对人工与模型标注者而言难度远超预期。利用句子-章节映射，测量摘要线性性（摘要保留原作事件顺序的程度）与一致性（摘要对原作的注意力分配均匀程度），通过分析摘要偏离线性性与一致性的时机及方式，明确文学作品与摘要在情节表达上的差异，尤其涉及叙事细节描述的清晰度与突出度。
英文摘要
Works of literature are complicated; they balance plot, suspense, surprise, and artistic expression. Summaries of literature prioritize plot, and therefore may deviate from their sources. Using a combination of manual and LLM-based annotation, we construct a dataset mapping sentences from 150 novel summaries to their respective source chapters. We find the task unexpectedly difficult for both human and model annotators. Using the sentence-to-chapter mappings, we then measure summary linearity, the degree to which it maintains the source's order of events, and uniformity, the degree to which a summary spreads attention equally across a source. By examining when and how summaries break linearity and uniformity, we identify differences in how literary works and summaries express plot, particularly with regard to the clarity and prominence with which narrative details are described.
