Abstract: Sentence alignment -- establishing links between corresponding sentences in two relateddocuments -- is as important in paraphrase generation as it is in machine translation. Despite its applicability, its benefits are often overlooked in the context of narrative understanding. For instance, it can be leveraged in cross-lingual story analysis and cultural analytics. This includes identifying similarities and differences between narratives across languages, or understanding narrative structures in a more comprehensive way. To bridge this gap, we introduce a novel methodology for sentence alignment designed specifically for novels. In particular, we propose Novalign, an end-to-end, fully-neural architecture that maps source and target sentences based on their contextualized sentence embeddings. We extensively evaluate Novalign on a new, multilingual dataset derived from the Opus project consisting of 20 language pairs, and demonstrate that our model achieves state-of-the-art performance. To ensure reproducibility, we release our code and model checkpoints at omitted.link.