  Most general-purpose extractive summarization models are trained on news
articles, which are short and present all important information upfront. As a
result, such models are biased on position and often perform a smart selection
of sentences from the beginning of the document. When summarizing long
narratives, which have complex structure and present information piecemeal,
simple position heuristics are not sufficient. In this paper, we propose to
explicitly incorporate the underlying structure of narratives into general
unsupervised and supervised extractive summarization models. We formalize
narrative structure in terms of key narrative events (turning points) and treat
it as latent in order to summarize screenplays (i.e., extract an optimal
sequence of scenes). Experimental results on the CSI corpus of TV screenplays,
which we augment with scene-level summarization labels, show that latent
turning points correlate with important aspects of a CSI episode and improve
summarization performance over general extractive algorithms leading to more
complete and diverse summaries.
