Writing a coherent and engaging story is not easy. Creative writers use their
knowledge and worldview to put disjointed elements together to form a coherent
storyline, and work and rework iteratively toward perfection. Automated visual
storytelling (VIST) models, however, make poor use of external knowledge and
iterative generation when attempting to create stories. This paper introduces
PR-VIST, a framework that represents the input image sequence as a story graph
in which it finds the best path to form a storyline. PR-VIST then takes this
path and learns to generate the final story via an iterative training process.
This framework produces stories that are superior in terms of diversity,
coherence, and humanness, per both automatic and human evaluations. An ablation
study shows that both plotting and reworking contribute to the model's
superiority.

本文介绍了 PR-VIST 框架，将输入的图像序列表示为故事图，找到形成故事情节的最佳路径，并通过迭代训练过程来生成最终故事，该框架根据自动和人工语法而言有更好的多样性、连贯性和人性化表现，削减研究表明描绘和重塑都对模型的优越性做出了贡献。