@inproceedings{zhang-pan-2026-readerscope,
title = "{R}eader{S}cope: From Offline Reader-Response Calibration to Online Controlled Story Generation",
author = "Zhang, Feng. and
Pan, Rong",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1074/",
pages = "24668--24712",
ISBN = "979-8-89176-459-0",
abstract = "Long-form story generation should not only maintain narrative coherence, but also shape how readers experience a story as it unfolds. However, existing LLM-based generators typically optimize plot structure, consistency, or local text quality, without explicitly modeling reader-response dynamics as generation-time control targets. To solve the problem, we introduce ReaderScope, a reader-response-guided framework that converts simulated reader-response trajectories into explicit control signals for controllable long-form story generation. ReaderScope calibrates temporal response patterns from reference novels across affective, suspenseful, immersive, and reflective dimensions, and uses these targets to guide iterative story revision. Experiments against representative long-form story generation baselines show that ReaderScope improves reader-facing story quality under both automatic and human pairwise evaluation, while ablation and trajectory-fidelity analyses confirm that the response signals are useful for generation and can be reliably controlled."
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<abstract>Long-form story generation should not only maintain narrative coherence, but also shape how readers experience a story as it unfolds. However, existing LLM-based generators typically optimize plot structure, consistency, or local text quality, without explicitly modeling reader-response dynamics as generation-time control targets. To solve the problem, we introduce ReaderScope, a reader-response-guided framework that converts simulated reader-response trajectories into explicit control signals for controllable long-form story generation. ReaderScope calibrates temporal response patterns from reference novels across affective, suspenseful, immersive, and reflective dimensions, and uses these targets to guide iterative story revision. Experiments against representative long-form story generation baselines show that ReaderScope improves reader-facing story quality under both automatic and human pairwise evaluation, while ablation and trajectory-fidelity analyses confirm that the response signals are useful for generation and can be reliably controlled.</abstract>
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%0 Conference Proceedings
%T ReaderScope: From Offline Reader-Response Calibration to Online Controlled Story Generation
%A Zhang, Feng.
%A Pan, Rong
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F zhang-pan-2026-readerscope
%X Long-form story generation should not only maintain narrative coherence, but also shape how readers experience a story as it unfolds. However, existing LLM-based generators typically optimize plot structure, consistency, or local text quality, without explicitly modeling reader-response dynamics as generation-time control targets. To solve the problem, we introduce ReaderScope, a reader-response-guided framework that converts simulated reader-response trajectories into explicit control signals for controllable long-form story generation. ReaderScope calibrates temporal response patterns from reference novels across affective, suspenseful, immersive, and reflective dimensions, and uses these targets to guide iterative story revision. Experiments against representative long-form story generation baselines show that ReaderScope improves reader-facing story quality under both automatic and human pairwise evaluation, while ablation and trajectory-fidelity analyses confirm that the response signals are useful for generation and can be reliably controlled.
%U https://aclanthology.org/2026.emnlp-main.1074/
%P 24668-24712
Markdown (Informal)
[ReaderScope: From Offline Reader-Response Calibration to Online Controlled Story Generation](https://aclanthology.org/2026.emnlp-main.1074/) (Zhang & Pan, EMNLP 2026)
ACL