  Unsupported and unfalsifiable claims we encounter in our daily lives can
influence our view of the world. Characterizing, summarizing, and -- more
generally -- making sense of such claims, however, can be challenging. In this
work, we focus on fine-grained debate topics and formulate a new task of
distilling, from such claims, a countable set of narratives. We present a
crowdsourced dataset of 12 controversial topics, comprising more than 120k
arguments, claims, and comments from heterogeneous sources, each annotated with
a narrative label. We further investigate how large language models (LLMs) can
be used to synthesise claims using In-Context Learning. We find that generated
claims with supported evidence can be used to improve the performance of
narrative classification models and, additionally, that the same model can
infer the stance and aspect using a few training examples. Such a model can be
useful in applications which rely on narratives , e.g. fact-checking.
