title: EIPE-text: Evaluation-Guided Iterative Plan Extraction for Long-Form
    Narrative Text Generation
summary: Plan-and-Write is a common hierarchical approach in long-form narrative text
  generation, which first creates a plan to guide the narrative writing.
  Following this approach, several studies rely on simply prompting large
  language models for planning, which often yields suboptimal results. In this
  paper, we propose a new framework called Evaluation-guided Iterative Plan
  Extraction for long-form narrative text generation (EIPE-text), which extracts
  plans from the corpus of narratives and utilizes the extracted plans to
  construct a better planner. EIPE-text has three stages: plan extraction,
  learning, and inference. In the plan extraction stage, it iteratively extracts
  and improves plans from the narrative corpus and constructs a plan corpus. We
  propose a question answer (QA) based evaluation mechanism to automatically
  evaluate the plans and generate detailed plan refinement instructions to guide
  the iterative improvement. In the learning stage, we build a better planner by
  fine-tuning with the plan corpus or in-context learning with examples in the
  plan corpus. Finally, we leverage a hierarchical approach to generate long-form
  narratives. We evaluate the effectiveness of EIPE-text in the domains of novels
  and storytelling. Both GPT-4-based evaluations and human evaluations
  demonstrate that our method can generate more coherent and relevant long-form
  narratives. Our code will be released in the future.
paper_authors._id: 6528981f94bb032403d407df
paper_authors.name: Wang You
paper_authors._id: 6528981f94bb032403d407e0
paper_authors.name: Wenshan Wu
paper_authors._id: 6528981f94bb032403d407e1
paper_authors.name: Yaobo Liang
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paper_authors.fullname: Yaobo Liang
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paper_authors._id: 6528981f94bb032403d407e2
paper_authors.name: Shaoguang Mao
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paper_authors.fullname: Shaoguang Mao
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paper_authors.user: dawnmsg
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paper_authors.name: Chenfei Wu
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paper_authors.fullname: Chenfei Wu
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paper_authors.user: Chenfei
paper_authors._id: 6528981f94bb032403d407e4
paper_authors.name: Maosong Cao
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paper_authors.name: Yuzhe Cai
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paper_authors.fullname: Yuzhe Cai
paper_authors.type: user
paper_authors.user: YZhe
paper_authors._id: 6528981f94bb032403d407e6
paper_authors.name: Yiduo Guo
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paper_authors.fullname: Guo
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paper_authors.user: YiDuo1999
paper_authors._id: 6528981f94bb032403d407e7
paper_authors.name: Yan Xia
paper_authors._id: 6528981f94bb032403d407e8
paper_authors.name: Furu Wei
paper_authors.status: admin_assigned
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paper_authors.fullname: Furu Wei
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paper_authors.user: thegenerality
paper_authors._id: 6528981f94bb032403d407e9
paper_authors.name: Nan Duan
paper_title: EIPE-text: Evaluation-Guided Iterative Plan Extraction for Long-Form
    Narrative Text Generation
paper_summary: Plan-and-Write is a common hierarchical approach in long-form narrative text
  generation, which first creates a plan to guide the narrative writing.
  Following this approach, several studies rely on simply prompting large
  language models for planning, which often yields suboptimal results. In this
  paper, we propose a new framework called Evaluation-guided Iterative Plan
  Extraction for long-form narrative text generation (EIPE-text), which extracts
  plans from the corpus of narratives and utilizes the extracted plans to
  construct a better planner. EIPE-text has three stages: plan extraction,
  learning, and inference. In the plan extraction stage, it iteratively extracts
  and improves plans from the narrative corpus and constructs a plan corpus. We
  propose a question answer (QA) based evaluation mechanism to automatically
  evaluate the plans and generate detailed plan refinement instructions to guide
  the iterative improvement. In the learning stage, we build a better planner by
  fine-tuning with the plan corpus or in-context learning with examples in the
  plan corpus. Finally, we leverage a hierarchical approach to generate long-form
  narratives. We evaluate the effectiveness of EIPE-text in the domains of novels
  and storytelling. Both GPT-4-based evaluations and human evaluations
  demonstrate that our method can generate more coherent and relevant long-form
  narratives. Our code will be released in the future.
paper_ai_summary: The proposed EIPE-text framework iteratively extracts and refines narrative plans from a corpus to improve long-form text generation, resulting in more coherent and relevant narratives.
paper_ai_keywords: plan extraction plan refinement plan corpus question answer fine-tuning in-context learning long-form narrative text generation