Creative writing is hard: Novelists struggle with writer's block daily. While automatic story generation has advanced recently, it is treated as a "toy task" for advancing artificial intelligence rather than helping people. In this paper, we create a system that produces a short description that narrates a predicted plot using existing story generation approaches. Our goal is to assist writers in crafting a consistent and compelling story arc. We conducted experiments on Amazon Mechanical Turk (AMT) to examine the quality of the generated story plots in terms of consistency and storiability. The results show that short descriptions produced by our frame-enhanced GPT-2 (FGPT-2) were rated as the most consistent and storiable among all models; FGPT-2's outputs even beat some random story snippets written by humans. Next, we conducted a preliminary user study using a story continuation task where AMT workers were given access to machine-generated story plots and asked to write a follow-up story. FGPT-2 could positively affect the writing process, though people favor other baselines more. Our study shed some light on the possibilities of future creative writing support systems beyond the scope of completing sentences. Our code is available at: https://github.com/appleternity/Story-Plot-Generation.

翻译：创意写作是困难的：小说家每天都在与写作障碍抗争。尽管自动故事生成技术近年来取得了进展，但它被更多地视为推动人工智能发展的“玩具任务”，而非真正帮助人类。本文构建了一个系统，利用现有故事生成方法生成叙述预测剧情的简短描述。我们的目标是协助作者构建连贯且引人入胜的故事弧。我们通过亚马逊土耳其机器人（AMT）进行实验，评估生成故事剧情在连贯性和可故事性方面的质量。结果表明，由我们增强的框架GPT-2（FGPT-2）生成的简短描述在所有模型中获得了最高的连贯性和可故事性评分；FGPT-2的输出甚至优于某些人工编写的随机故事片段。随后，我们开展了一项初步用户研究，采用故事续写任务，让AMT工作者访问机器生成的故事剧情并撰写后续故事。尽管人们更偏好其他基线模型，但FGPT-2仍能对写作过程产生积极影响。本研究为超越句子补全范畴的未来创意写作支持系统提供了可能性。我们的代码已开源：https://github.com/appleternity/Story-Plot-Generation。