StoryLens: Preference-Aligned Story Rewriting via Context-Aware Narrative Enrichment
- URL: http://arxiv.org/abs/2605.28073v1
- Date: Wed, 27 May 2026 07:29:37 GMT
- Title: StoryLens: Preference-Aligned Story Rewriting via Context-Aware Narrative Enrichment
- Abstract summary: We argue that effective story rewriting demands context-aware narrative enrichment beyond surface-level stylistic adaptation.<n>Our pilot human study shows that style adaptation alone provides only marginal gains in reader satisfaction.<n>We introduce STORYLENLENNCH, a large-scale benchmark for preference-aligned story rewriting.<n>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.
- Score: 46.52328522235641
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- STORYANCHORS: Generating Consistent Multi-Scene Story Frames for Long-Form Narratives [82.19488717416351]
This paper introduces StoryAnchors, a unified framework for generating high-quality, multi-scene story frames.<n>StoryAnchors employs a bidirectional story generator that integrates both past and future contexts to ensure temporal consistency.<n>It also integrates Multi-Event Story Frame Labeling and Progressive Story Frame Training, enabling the model to capture both overarching narrative flow and event-level dynamics.
arXiv Detail & Related papers (2025-05-13T08:48:10Z) - Pastiche Novel Generation Creating: Fan Fiction You Love in Your Favorite Author's Style [48.08453439104245]
We propose WriterAgent, a novel generation system designed to master the core aspects of literary pastiche.<n>WriterAgent is trained through a curriculum learning paradigm, progressing from low-level stylistic mastery to high-level narrative coherence.<n>We evaluate WriterAgent on multilingual classics like Harry Potter and Dream of the Red Chamber, demonstrating its superiority over baselines.
arXiv Detail & Related papers (2025-02-21T17:40:42Z) - Collective Critics for Creative Story Generation [9.249662593315541]
We propose Collective Critics for Creative Story Generation framework (CritiCS)
CritiCS is composed of plan refining stage (CrPlan) and story generation stage (CrText)
In each stage, a group of LLM critics and one leader collaborate to incrementally drafts of plan and story throughout multiple rounds.
arXiv Detail & Related papers (2024-10-03T12:21:17Z) - Generating Visual Stories with Grounded and Coreferent Characters [63.07511918366848]
We present the first model capable of predicting visual stories with consistently grounded and coreferent character mentions.<n>Our model is finetuned on a new dataset which we build on top of the widely used VIST benchmark.<n>We also propose new evaluation metrics to measure the richness of characters and coreference in stories.
arXiv Detail & Related papers (2024-09-20T14:56:33Z) - ContextualStory: Consistent Visual Storytelling with Spatially-Enhanced and Storyline Context [50.572907418430155]
ContextualStory is a framework designed to generate coherent story frames and extend frames for visual storytelling.<n>We introduce a Storyline Contextualizer to enrich context in storyline embedding, and a StoryFlow Adapter to measure scene changes between frames.<n>Experiments on PororoSV and FlintstonesSV datasets demonstrate that ContextualStory significantly outperforms existing SOTA methods in both story visualization and continuation.
arXiv Detail & Related papers (2024-07-13T05:02:42Z) - Returning to the Start: Generating Narratives with Related Endpoints [27.61802620856587]
We propose RENarGen, a story-generation paradigm that generates narratives by ensuring the first and last sentences are related and then infilling the middle sentences.
Our contributions include an initial exploration of how various methods of bookending from Narratology affect language modeling for stories.
arXiv Detail & Related papers (2024-03-31T23:48:50Z) - Re3: Generating Longer Stories With Recursive Reprompting and Revision [83.99558005056817]
We consider the problem of automatically generating longer stories of over two thousand words.
Compared to prior work on shorter stories, long-range plot coherence and relevance are more central challenges here.
We propose the Recursive Reprompting and Revision framework (Re3) to address these challenges.
arXiv Detail & Related papers (2022-10-13T06:29:57Z) - Stylized Story Generation with Style-Guided Planning [38.791298336259146]
We propose a new task, stylized story gen-eration, namely generating stories with speci-fied style given a leading context.
Our model can controllably generateemo-tion-driven or event-driven stories based on the ROCStories dataset.
arXiv Detail & Related papers (2021-05-18T15:55:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.