Storytelling and narrative are fundamental to human experience, intertwined with our social and cultural engagement. As such, researchers have long attempted to create systems that can generate stories automatically. In recent years, powered by deep learning and massive data resources, automatic story generation has shown significant advances. However, considerable challenges, like the need for global coherence in generated stories, still hamper generative models from reaching the same storytelling ability as human narrators. To tackle these challenges, many studies seek to inject structured knowledge into the generation process, which is referred to as structured knowledge-enhanced story generation. Incorporating external knowledge can enhance the logical coherence among story events, achieve better knowledge grounding, and alleviate over-generalization and repetition problems in stories. This survey provides the latest and comprehensive review of this research field: (i) we present a systematical taxonomy regarding how existing methods integrate structured knowledge into story generation; (ii) we summarize involved story corpora, structured knowledge datasets, and evaluation metrics; (iii) we give multidimensional insights into the challenges of knowledge-enhanced story generation and cast light on promising directions for future study.

翻译：叙事与故事讲述是人类经验的基础，与社会和文化参与交织在一起。因此，研究者长久以来致力于构建能够自动生成故事的系统。近年来，在深度学习与大规模数据资源的推动下，自动故事生成取得了显著进展。然而，诸如生成故事的全局一致性等重大挑战，仍阻碍着生成模型达到人类叙述者同等的故事讲述能力。为应对这些挑战，许多研究尝试将结构化知识注入生成过程，即所谓的结构化知识增强故事生成。引入外部知识可增强故事事件间的逻辑一致性，实现更好的知识锚定，并缓解故事中的过度泛化和重复问题。本综述提供了该研究领域的最新全面回顾：(i) 我们提出了关于现有方法如何将结构化知识集成到故事生成中的系统分类体系；(ii) 我们总结了所涉及的故事语料库、结构化知识数据集及评估指标；(iii) 我们针对知识增强故事生成的挑战提供了多维度见解，并为未来研究指明了有前景的方向。