基于结构化知识增强的开放世界故事生成：一项全面综述
Open-world Story Generation with Structured Knowledge Enhancement: A Comprehensive Survey
AI总结：
本综述全面回顾了结构化知识增强的开放世界故事生成领域，提出了现有方法的系统分类法，总结了相关数据集与评估指标，并探讨了当前挑战与未来研究方向。
摘要：
讲故事与叙事是人类体验的基础，与我们的社会和文化参与交织在一起。因此，研究人员长期以来一直试图创建能够自动生成故事的系统。近年来，在深度学习和海量数据资源的推动下，自动故事生成取得了显著进展。然而，相当大的挑战，如生成故事需要全局连贯性，仍然阻碍生成模型达到与人类讲述者相同的故事讲述能力。为了应对这些挑战，许多研究试图将结构化知识注入生成过程，这被称为结构化知识增强的故事生成。结合外部知识可以增强故事事件之间的逻辑连贯性，实现更好的知识基础，并缓解故事中的过度概括和重复问题。本综述提供了该研究领域的最新和全面回顾：(i) 我们提出了关于现有方法如何将结构化知识整合到故事生成中的系统分类法；(ii) 我们总结了涉及的故事语料库、结构化知识数据集和评估指标；(iii) 我们对知识增强故事生成的挑战提供了多维度的见解，并为未来研究指明了有前景的方向。
英文摘要：
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 systematic 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
