Abstract: We present a novel approach to modeling narratives using narrative chain embeddings.A new dataset of narrative chains extracted from German news texts is presented.With neural methods, we produce models for both German and English that achieve state-of-the-art performance on the Multiple Choice Narrative Cloze task.Subsequently, we perform an extrinsic evaluation of the embeddings our models produce and show that they perform rather poorly in identifying narratively similar texts. We explore some of the reasons for this underperformance and discuss the upsides of our approach.We provide an outlook on alternative ways to model narratives, as well as techniques for evaluating such models.