  Sharing personal narratives is a fundamental aspect of human social behavior
as it helps share our life experiences. We can tell stories and rely on our
background to understand their context, similarities, and differences. A
substantial effort has been made towards developing storytelling machines or
inferring characters' features. However, we don't usually find models that
compare narratives. This task is remarkably challenging for machines since
they, as sometimes we do, lack an understanding of what similarity means. To
address this challenge, we first introduce a corpus of real-world spoken
personal narratives comprising 10,296 narrative clauses from 594 video
transcripts. Second, we ask non-narrative experts to annotate those clauses
under Labov's sociolinguistic model of personal narratives (i.e., action,
orientation, and evaluation clause types) and train a classifier that reaches
84.7% F-score for the highest-agreed clauses. Finally, we match stories and
explore whether people implicitly rely on Labov's framework to compare
narratives. We show that actions followed by the narrator's evaluation of these
are the aspects non-experts consider the most. Our approach is intended to help
inform machine learning methods aimed at studying or representing personal
narratives.
