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Plan-and-Write is a common hierarchical approach in long-form narrative textgeneration, which first creates a plan to guide the narrative writing.Following this approach, several studies rely on simply prompting largelanguage models for planning, which often yields suboptimal results. In thispaper, we propose a new framework called Evaluation-guided Iterative PlanExtraction for long-form narrative text generation (EIPE-text), which extractsplans from the corpus of narratives and utilizes the extracted plans toconstruct a better planner. EIPE-text has three stages: plan extraction,learning, and inference. In the plan extraction stage, it iteratively extractsand improves plans from the narrative corpus and constructs a plan corpus. Wepropose a question answer (QA) based evaluation mechanism to automaticallyevaluate the plans and generate detailed plan refinement instructions to guidethe iterative improvement. In the learning stage, we build a better planner byfine-tuning with the plan corpus or in-context learning with examples in theplan corpus. Finally, we leverage a hierarchical approach to generate long-formnarratives. We evaluate the effectiveness of EIPE-text in the domains of novelsand storytelling. Both GPT-4-based evaluations and human evaluationsdemonstrate that our method can generate more coherent and relevant long-formnarratives. Our code will be released in the future.

EIPE-text: Evaluation-Guided Iterative Plan Extraction for Long-Form Narrative Text Generation