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Storyline Trees: Hierarchical Representations for Long-Form Narratives

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arXiv:2606. 20900v1 Announce Type: new Abstract: Long-form narratives are challenging for long-context models because their structure is implicit: events, characters, and plotlines interact across hundreds of pages without the explicit cues that guide navigation in structured documents.

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Authors: Litu Ou, Mirella Lapata
Released: 2026-06-23 (16d ago)
Topic: Related to current project
Relation to my work: Read next because Storyline Trees: Hierarchical Representations for Long-Form Narratives overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: strong, text, under, eval, line, without, trained, model. Source: arxiv cs.CL (NLP).
arXiv: 2606.20900

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