将多视角叙事整合为连贯时序文本，解决传统摘要遗漏时间线的问题。
Narrative Consolidation: Formulating a New Task for Unifying Multi-Perspective Accounts
- 构建事件时序图（TAEG）显式建模时间顺序与事件对齐
- 在四福音书上实现完美时序排序（肯德尔τ=1.000），ROUGE-L提升357.2%
- 适合历史、法律等需还原完整叙事脉络的研究场景
处理重叠的叙述性文档（如法律证词或历史记载）的目标并非压缩，而是生成统一、连贯且时序正确的文本。标准多文档摘要（MDS）强调简洁性，难以保留叙事流。本文首次正式定义此挑战为新任务：叙事整合（Narrative Consolidation），核心目标为时序完整性、内容完备性及互补细节融合。为凸显时间结构的重要性，提出时序对齐事件图（TAEG），显式建模时间与事件对齐关系。通过在TAEG上应用标准中心性算法，实现事件表示的优选机制，确保每事件在正确时序位置被选择。在四福音书研究中，该方法设计上保证完全时序正确（肯德尔τ=1.000），内容指标显著提升（如ROUGE-L F1提升357.2%）。该基线方法的成功验证了叙事整合作为有效任务的合理性，并确立显式时序骨架是其解决的关键基础。
原文摘要 · Abstract (English)
Processing overlapping narrative documents, such as legal testimonies or historical accounts, often aims not for compression but for a unified, coherent, and chronologically sound text. Standard Multi-Document Summarization (MDS), with its focus on conciseness, fails to preserve narrative flow. This paper formally defines this challenge as a new NLP task: Narrative Consolidation, where the central objectives are chronological integrity, completeness, and the fusion of complementary details. To demonstrate the critical role of temporal structure in this task, we introduce Temporal Alignment Event Graph (TAEG), a graph structure that explicitly models chronology and event alignment. By applying a standard centrality algorithm to TAEG, our method functions as a version selection mechanism, choosing the most central representation of each event in its correct temporal position. In a study on the four Biblical Gospels, this structure-focused approach guarantees perfect temporal ordering (Kendall's Tau of 1.000) by design and dramatically improves content metrics (e.g., +357.2% in ROUGE-L F1). The success of this baseline method validates the formulation of Narrative Consolidation as a relevant task and establishes that an explicit temporal backbone is a fundamental component for its resolution.
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