Dialogue understanding and abstractive summarization remain both important and challenging problems for computational linguistics.
In this paper, we contribute the \crddd~(\crd), a linguistically rich dataset with dialogue extracted from the unscripted, live-streamed show \CR and long, abstractive summaries extracted from the
\CR Fandom wiki. We provide a data augmentation method to help the community start
modeling and evaluation for the dialogue summarization task and discuss the initial modeling benchmark results. We find current paradigms in summarization modeling to have specific failures in capturing semantics and pragmatics, content selection, rewriting, and evaluation in the domain of long, story-telling dialogue. 
We hope \crd~offers useful, unique data for the community to further explore dialogue modeling and summarization.
We also hope that the dataset can be added to in the future with multi-modal extractions, more granular annotations, and deeper mining of the wiki.
