In this short paper, we compare existing value systems and approaches in NLP and HCI for collecting narrative data. Building on these parallel discussions, we shed light on the challenges facing some popular NLP dataset types, which we discuss these in relation to widely-used narrative-based HCI research methods; and we highlight points where NLP methods can broaden qualitative narrative studies. In particular, we point towards contextuality, positionality, dataset size, and open research design as central points of difference and windows for collaboration when studying narratives. Through the use case of narratives, this work contributes to a larger conversation regarding the possibilities for bridging NLP and HCI through speculative mixed-methods.