  Many tasks aim to measure machine reading comprehension (MRC), often focusing
on question types presumed to be difficult. Rarely, however, do task designers
start by considering what systems should in fact comprehend. In this paper we
make two key contributions. First, we argue that existing approaches do not
adequately define comprehension; they are too unsystematic about what content
is tested. Second, we present a detailed definition of comprehension -- a
"Template of Understanding" -- for a widely useful class of texts, namely short
narratives. We then conduct an experiment that strongly suggests existing
systems are not up to the task of narrative understanding as we define it.
