Artificial intelligence applied to human conversation remains an incredibly challenging task in computer science. 
Task-oriented dialogues, which are more narrowly scoped and information dense than conversational dialogue, have been the focus of recent progress in dialogue understanding \cite{budzianowski-etal-2018-multiwoz}.
% Though there has been recent progress in dialogue understanding, the focus has been on task-oriented dialogues 3ai that are more narrowly scoped and more information dense than casual human conversation. 
A difficulty for hypothesis testing on non-task oriented dialogues is a lack of large datasets that are fully representative of the spontaneity and noise of real world conversation, especially in the areas of storytelling and narrative beyond long-form text or monologue.
% Non task-oriented progress has also been made, but most of the hypothesis testing has been built on top of datasets that are still wanting in topics discussed per dialogue, spontaneity, and noise, when compared to real world conversation.
% Another important aspect of human conversation is storytelling and narrative beyond that of a long-form text or monologue. 
Many potential dialogue processing tasks involve multi-speaker dialogues where narrative elements are conveyed through interaction between two or more speakers.
These narrative elements can include changes in the states of narrative objects, descriptions of events, or changes in the states of speakers themselves. 
Some explored sub-tasks for narrative understanding are topic understanding, character state tracking, and abstractive summarization.
Though progress has been made in these areas, it has been on datasets where conversation has been constrained to specific topics, constrained by medium of communication, or scripted (in the case of television or movies) \cite{forchini2009spontaneity}. With datasets that involve naturally occurring dialogue, the small amount of data per narrative or speaker makes modeling challenging.
% Progress in these areas have been made as well; but they are typically also on datasets that are shorter and more focused in scope or datasets from television or movies where the time constraints and scripted nature make the dialogues less noisy and more information dense.

\subsection{\CR Episodes and Wiki}
The \CR show\footnote{\href{https://critrole.com}{critrole.com}} is a weekly unscripted, live-stream of a fixed group of people playing Dungeons and Dragons, a popular role-playing game. \CR is set in a fictional world created by the Dungeon Master (DM) Matthew Mercer. 

Separate from Matthew, there are eight other players who participate in his world as role-played characters; whose actions in the game influence the fictional world (as per the DM) along with their own character's state. There are multiple objectives to the game, both hidden and explicitly stated by both parties. For example, the DM might explicitly state a quest for the players to complete or a player's character might have an explicit personal goal that needs to be met. Examples of implicit objectives are non-player character’s objectives created by the DM, and a player's character's backstory that influence their actions. This definition and expansion of the fictional world, the interaction with the world, and the development of the narrative is done entirely through unscripted spoken dialogue between the DM and the other players. 

Fans have maintained dialogue transcriptions for each episode as well as an online knowledge base (the Fandom wiki\footnote{\href{https://criticalrole.fandom.com}{criticalrole.fandom.com}}) where details about the players, characters, world, and game sessions are continuously added to. By extracting dialogues from the \CR transcripts, \crd~aims to provide the community with a narrative-centered dataset that is unscripted, noisy, and spontaneous; while being coherent, consistent in latent speaker attributes and personalities, and considerably longer in dialogue length than similar conversational dialogue datasets. From the wiki, we obtain human-authored, structured summaries for each episode that support tasks of narrative understanding and extraction, topic understanding and segmentation, and summarization from conversational dialogue.
\subsection{Contributions}
We make five contributions in this paper. First, we produce a cleaned and structured dialogue dataset extracted from the \CR transcripts (\crd-Dialogues)\footnote{\href{https://github.com/RevanthRameshkumar/CRD3}{github.com/RevanthRameshkumar/CRD3}}. Second, we provide corresponding structured abstractive summaries for each episode, mined from the Fandom wiki (\crd-Summaries).
% Most metadata blocks include a short abstractive summary for the narrative, a much longer abstractive summary broken up into parts, and a ‘Previously on Critical Role’ summary. 
Third, we analyze the dataset and compare it to similar datasets. Fourth, we describe our method of data augmentation via text alignment to make this data scale-appropriate for neural ML approaches, and provide these summary-dialogue chunk pairs (\crd-SD-pairs). Finally, we construct an abstractive summarization baseline from these pairs and discuss its evaluation (\crd-Baseline). 

We believe that better abstractive summarization tools to distill information is essential given the ongoing growth of unscripted, multi-person dialogues in entertainment and business scenarios. We hope that \crd~will support research and development for such tools.