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arXiv Machine Learning By Roger A. Finger, Eduardo G. Cortes, Sandro J. Rigo, Gabriel de O. Ramos

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

Read the original on arXiv Machine Learning →

The paper introduces Narrative Consolidation, a new NLP task that aims to merge overlapping narrative documents—such as legal testimonies or historical accounts—into a single, chronologically coherent text, rather than merely compressing them. It defines the task, proposes an evaluation framework, and presents the Gospel Consolidation Language Resource, a benchmark built from the four Biblical Gospels with 169 canonical events and cross‑document alignments. Experiments show that providing an explicit temporal backbone dramatically improves performance, a simple length heuristic outperforms graph‑based methods, and temporal edges are the key discriminative signal.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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