% Disregarding machine-to-machine dialogue, dialogue can be roughly grouped into human-to-human and human-to-machine dialogues. These can consist of a subset of task oriented, conversational, written, spoken, scripted, spontaneous, and constrained dialogues. As most dialogue datasets and methods can span several categories, we organize them with regards to the key properties of the CRD3 dataset.
The \crddd~is a combination of story-telling dialogues structured around the game-play of Dungeons and Dragons and corresponding abstractive summarizations for each dialogue. As such, it can be compared to existing dialogue datasets and summarization datasets.
\subsection{Dialogue Datasets}
There are currently many existing dialogue datasets (disregarding machine-to-machine) that can be roughly grouped into task-oriented, conversational, scripted, constrained, and spontaneous dialogues \cite{serban2015survey}. Task-oriented datasets address specific tasks and are constrained by an ontology \cite{budzianowski-etal-2018-multiwoz}. If the task is sufficiently constrained, even a human-to-human task-oriented dialogue can lack spontaneity and noise of open domain conversation \cite{haber-etal-2019-photobook}, \cite{vaidyanathan-etal-2018-snag}, \cite{lison2016opensubtitles2016}. Agents trained on such datasets cannot be expected to model spontaneous conversational dialogue.
% \textbf{Task oriented dialogue}
% The motivation of task-oriented dialogue systems is primarily that of automated conversational agents being able to handle specific tasks, in one or more domains. Generally, the set of tasks is specific and constrained, and the domains are defined by an ontology \cite{budzianowski-etal-2018-multiwoz}. Since the conversational agent is bound by the ontology, the interaction with the agent is implicitly bound by the given ontology. If the task that is used to build the dataset is sufficiently constrained \cite{haber-etal-2019-photobook}, \cite{vaidyanathan-etal-2018-snag}, \cite{lison2016opensubtitles2016}, even a human-to-human task-oriented dialogue can lack the spontaneity and noise of open, or loosely defined, domain conversation. Although a conversational agent trained on task-oriented dialogue can fulfill its purpose regarding task-oriented scenarios, it cannot be expected to model spontaneous, conversational dialogue.
Scripted dialogue datasets are closer to conversational dialogue. Popular scripted dialogues come from TV shows, movies, and novels; sometimes featuring further annotations \cite{poria-etal-2019-meld}, \cite{lison2016opensubtitles2016}, \cite{banchs-2012-movie}. Though the lack of noise can be helpful in training a dialogue system, they do contain artificialities in their linguistic properties \cite{forchini2009spontaneity}. With datasets that do have natural conversation, either with provided topics \cite{rashkin-etal-2019-towards}, \cite{Godfrey1992Switch}, \cite{Carletta2005AMI} or truly naturally occurring \cite{ritter-etal-2010-unsupervised},\cite{schrading-etal-2015-analysis}, \cite{li-etal-2017-dailydialog}, \cite{leech1992100}, \cite{misra-etal-2015-using}, the larger scope and noise along with the small amount of data for individual domains, latent speaker attributes, and linguistic attributes make tasks like response generation, abstractive summarization, and speaker personality modeling more difficult \cite{vinyals2015neural}, \cite{black2011spoken}, \cite{stent2005evaluating}, \cite{Poria2019EmotionRI}. Story-telling and game-playing dialogues can have properties from both task-oriented and conversational dialogues, as they have specific topics or tasks and are primarily human-to-human \cite{Gratch2007CreatingRW}, \cite{Hung2009TheIW}, \cite{afantenos2012developing}, \cite{djalali2012corpus}, \cite{hu-etal-2016-corpus}. In story-telling dialogues there is a clear topic constraint and purpose of conveying narratives. In game-play dialogues, there are clear tasks that the speakers try to complete, to either win or progress the game. This helps reduce topic noise and increase information density, but retains natural noise like disfluencies, false starts, fragments, and spontaneity.
% Closer to conversational dialogue are scripted dialogue datasets. though they are more compact and contain artificialities
% \textbf{Scripted Dialogue}
% A step away from task-oriented dialogues with regards to topic constraints and dialogue noise is scripted dialogue extracted from works of fiction. Though these dialogues are compact in nature and contain artificialities in their linguistic properties; they are closer to natural human dialogue \cite{forchini2009spontaneity}. The lack of noise might even be helpful in training a dialogue system if the corpus size is constrained. For example, the exaggerated emotion for clear communication with the audience in TV shows might help the performance of an emotion classification model \cite{poria2018meld}. Popular scripted dialogues come from TV shows, movies, and novels; sometimes featuring further annotations \cite{poria2018meld}, \cite{lison2016opensubtitles2016}, \cite{banchs-2012-movie}. When extracted from TV or movies, they also have the advantage of being multi-modal in nature.
% \textbf{Spontaneous dialogue}
% Most human conversation is spontaneous, and the topic is usually unspecified or very loosely specified. The datasets that are extracted from this type of dialogue are closest in linguistic properties (both spoken and written) to naturally occurring human dialogue. Some are truly naturally occurring \cite{ritter-etal-2010-unsupervised},\cite{schrading-etal-2015-analysis}, \cite{li-etal-2017-dailydialog}, \cite{leech1992100}, \cite{misra-etal-2015-using} where there was no potential for subject bias. Others are very close to naturally occurring, where the conversation topic was either not specified or loosely specified \cite{rashkin-etal-2019-towards}, \cite{Godfrey1992Switch}, \cite{Carletta2005AMI}. Though lacking in artificialities and possible exaggeration like datasets from scripted dialogues, and closer to true human conversation in latent features, the challenge with these types of datasets is the larger scope and noise. While information and representations can still be learned from these datasets, the long tail of domains, latent speaker attributes, and linguistic attributes make tasks like response generation, abstractive summarization, and speaker personality modeling more difficult \cite{vinyals2015neural}, \cite{black2011spoken}, \cite{stent2005evaluating}, \cite{Poria2019EmotionRI}.
% \textbf{Constrained Dialogue}
% Two types of dialogues that have properties from both task-oriented dialogues and conversational dialogues are dialogues with the intent of story-telling or game playing. These dialogues can be categorized as constrained dialogues, where the conversation is about a specific topic or is intended to solve a specific task \cite{serban2015survey}. In story-telling dialogues there is an implicit or explicit topic that the progression of the dialogue is usually constrained by and the primary purpose of the dialogue is to convey a narrative. In dialogues that are also constrained by time, especially in scripted dialogues, we can expect less topic noise and higher information density while retaining some of the natural noise (disfluencies, false starts, fragments, etc) and spontaneity of natural conversation \cite{Gratch2007CreatingRW}. Game playing dialogue is similar in that the participants are limited in dialogue topics. Games can also be task oriented where each participant strives to fulfill individual goals or group goals, with the possibility of hidden goals \cite{Hung2009TheIW}, \cite{afantenos2012developing}, \cite{djalali2012corpus}.

\crd~has extensive storytelling and narrative building through dialogue, as well as game-playing since Dungeons and Dragons is the show's focus. The episodes are unscripted and live-streamed, so the dialogue is naturally occurring and contains a large amount of context-switching and chit-chat. Since it is spoken then transcribed to text, there exists linguistic noise as usually present in naturally spoken dialogue. Finally, the large amount of turns combined with consistent cast and persistent environments make modelling based on latent speaker and linguistic attributes more feasible.

\subsection{Abstractive Summarization Datasets}
Most of the recent abstractive summarization research is conducted on document datasets (news, scientific papers, and patents) \cite{Hermann:2015:TMR:2969239.2969428}, \cite{Cohan_2018}, \cite{Sharma_2019}. However, the methods used to perform well in these domains are less effective in dialogue (movies, personal-interviews, multi-person dialogues, etc) \cite{kedzie-etal-2018-content}. As \cite{narayan2018don} noted, many of the current summarization datasets highly reward extractive approaches due to the large amount of phrasal overlap in document and summary. 
% The advantage with these datasets is the large volumes that make them amenable to neural approaches. “Extreme” summarization datasets like XSum aim to provide new challenges for summarization systems by providing short, minimally overlapping summaries for documents. We contrast this with the summaries for the Critical Role dialogues, where we find a significant amount of n-gram overlap. But by virtue of transformation from dialogue to third-person summary, a largely extractive summarization method is not appropriate.
Dialogue summarization is under-explored in datasets. For abstractive summarization, the most popular spoken dialogue datasets are AMI and Switchboard. Others exist, but are more constrained or purely textual, \cite{zhou-etal-2018-dataset}, \cite{gella-etal-2018-dataset}, \cite{misra-etal-2015-using}, \cite{louis-sutton-2018-deep}, \cite{pan2018dial2desc}. Notably, \cite{gorinski-lapata-2015-movie}, \cite{gorinski-lapata-2018-whats} combine movie scripts with Wikipedia plot summaries and other metadata. Though this brings us closer to longer form abstractive dialogue summarization data, there is significant information about the plot conveyed through script notes and descriptions, and not spoken dialogue.
% \textbf{Dialogue Centered Datasets} 
% losing the spontaneity from spoken dialogue
% One reason is the grammatical person switch from dialogue to summary.
% Some of the more recent methods that show promise \cite{Liu:2019:ADS:3292500.3330683} conduct research on internal corporate datasets that, while very large in volume, are not available publicly.