Specific discourse types present their own special challenges across the spectrum of NLP techniques, from models that are trained or tuned on specific types of discourse (e.g., wall street journal articles), to techniques making certain assumptions about the text (e.g., that everything described takes places “in the real world”). The narrative discourse form presents numerous interesting situations that both challenge the capabilities of existing techniques, and also suggest novel, NLP tasks that are specifically relevant to narrative. I review recent progress in the Cognac Laboratory on NLP as applied to narrative. I discuss four tasks. First, story detection, a variation of the text classification task where the goal is to identify whether a text contains a narrative. Second, animacy and character detection, where the goal is to determine whether a referent is animate and is acting as a “character”. We see that this approach requires some narratological sophistication to be successful. Third, new improvements in sub-event detection on narrative texts that take advantage of certain important features of narrative discourse. And, fourth, new approaches to timeline extraction that significantly improve our ability to extract, organize, and characterize timelines of events. This collection of results represents concrete steps toward our ability to “do text2story”, and points the way forward to an approach to NLP that is truly “narratologically aware.”