  Stories about everyday situations are an essential part of human
communication, motivating the need to develop AI agents that can reliably
understand these stories. Despite the long list of supervised methods for story
completion and procedural understanding, current AI has no mechanisms to
automatically track and explain procedures in unseen stories. To bridge this
gap, we study the ability of AI models to transfer procedural knowledge to
novel narrative tasks in a transparent manner. We design LEAP: a comprehensive
framework that integrates state-of-the-art modeling architectures, training
regimes, and augmentation strategies based on both natural and synthetic
stories. To address the lack of densely annotated training data, we devise a
robust automatic labeler based on few-shot prompting to enhance the augmented
data. Our experiments with in- and out-of-domain tasks reveal insights into the
interplay of different architectures, training regimes, and augmentation
strategies. LEAP's labeler has a clear positive impact on out-of-domain
datasets, while the resulting dense annotation provides native explainability.
