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The paper introduces STORYLENS, a benchmark and model for preference-aligned story rewriting, emphasizing context-aware narrative enrichment over simple style transfer. They found that enriching the narrative context leads to a significant 24.5% improvement in reader satisfaction compared to a marginal 2.3% gain from style adaptation alone. To achieve this, they use a two-stage rewriting model (STORYLENSWRITER) combining supervised fine-tuning with GRPO-based reinforcement learning, trained and evaluated on their new STORYLENSBENCH dataset.
Style transfer alone is a red herring in story rewriting; context-aware narrative enrichment delivers a 10x boost in reader satisfaction.
Story rewriting aims to adapt existing narratives to diverse reader preferences while preserving plot consistency and narrative coherence. Unlike conventional work on style transfer, we argue that effective story rewriting demands context-aware narrative enrichment beyond surface-level stylistic adaptation. Our pilot human study shows that style adaptation alone provides only marginal gains in reader satisfaction (2.3%), while context-enhanced rewriting substantially improves user preference alignment (24.5%). Motivated by this, we introduce STORYLENSBENCH, a large-scale benchmark for preference-aligned story rewriting, comprising structured story books, multi-dimensional reader preference profiles, and ranked context-aware rewritten stories. Building on this benchmark, we propose STORYLENSEVAL, a reward model for estimating reader satisfaction over rewritten stories, and STORYLENSWRITER, a two-stage rewriting model combining supervised fine-tuning with GRPO-based reinforcement learning. We further establish a comprehensive evaluation framework covering fidelity, coherence, and reader satisfaction. Experimental results demonstrate that STORYLENSWRITER consistently outperforms strong generation and personalization baselines, highlighting the importance of context-aware narrative enrichment for personalized story rewriting.