Narrative Consolidation: Formulating a New Task for Unifying Multi-Perspective Accounts

Abstract: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, focusing on chronological integrity, completeness, and the fusion of complementary details. We establish the resources needed to study it: a formal task definition, an evaluation paradigm, the Gospel Consolidation Language Resource -- a benchmark built from the four Biblical Gospels with 169 canonical events, cross-document alignments, and a reference consolidation -- and a suite of reference systems, ranging from timeline-agnostic heuristics to the Temporal Alignment Event Graph (TAEG). Benchmarking yields three findings. First, the explicit temporal backbone is the dominant factor: every system granted the canonical timeline raises ROUGE-L F1 from 0.206 to at least 0.81. Second, on a fusion-style reference, a simple length heuristic is remarkably strong (0.947 ROUGE-L F1), outperforming graph-based selection (0.846). Third, ablations show the discriminative signal resides in temporal edges, while intra-cluster lexical similarity is uninformative. These results establish Narrative Consolidation as a distinct task and pose an open challenge: to surpass that heuristic with a principled selection mechanism and true fusion of complementary details.

Computation and Language (cs.CL); Machine Learning (cs.LG)

arXiv:2512.18041 [cs.CL]

(or arXiv:2512.18041v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2512.18041 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.5753/jbcs.2026.7717 Focus to learn more DOI(s) linking to related resources

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