\subsection{Dungeons and Dragons}
Briefly, Dungeons and Dragons is a popular role-playing game that is driven by structured storytelling. Players create characters to participate in a fictional world created by the Dungeon Master (DM). They interact with the world entirely through dialogue with the DM and use dice rolls as a way to introduce randomness to the consequences of their actions. Actions can include exploring the environment, talking to fictional characters (role played by the DM), battle, and puzzle solving.\footnote{\href{https://dnd.wizards.com/dungeons-and-dragons}{dnd.wizards.com/dungeons-and-dragons}}
\subsection{\CR Video Stream Transcripts}
The CRD3 dataset consists of 159 episodes (dialogues) from two campaigns. 
Campaign 1 has 113 episodes and Campaign 2 has 46 episodes, with new episodes being actively added. The episodes are unscripted and live-streamed, then archived and transcribed; they are usually several hours long.
Detailed episode information can be found on the Fandom wiki\footnote{\href{https://criticalrole.fandom.com/wiki/List\_of\_episodes}{criticalrole.fandom.com/wiki/List\_of\_episodes}}.
% (including airdate, runtime, speaker bios, etc) 
The episodes usually start with some out-of-narrative logistics, then proceed to the actual D\&D game where the players communicate character action by in-character role-playing or by describing the characters' actions in third person. There is also substantial out of narrative chit-chat and context switching.

For each episode, we extract the names and turns from the dialogue transcript and clean the data as much as possible. 
We try to resolve the inconsistencies in spelling of speaker names, use of quotes, onomatopoeia, speaker aliases (and character aliases), parse multiple speakers for turns if needed, and others that exist due to the transcripts being written over time by fans.
We also replace all instances of character aliases in the speaker field with the real speakers' names to reduce noise. 
Along with the cleaned data, we provide the raw transcription data to document the changes via diff. 
% Transcripts of 'one-shots', D\&D sessions not part of an overarching campaign, are not  include them in this version of the dataset.
\subsection{\CR Episode Summaries}
\label{sec:summary description}
The summaries for each episode were mined from the \CR Fandom wiki. 
The summaries are unique in that they are structured and offer different levels of summarization. 
Most episodes have a (1)~wiki opening blurb, which offers briefest level of summarization. 
This is followed by a synopsis section which is (usually) comprised of several parts: (2)~pre-show and announcements, where some logistical information is mentioned; (3)~recap, where the previous episode is summarized (usually done by Matt in the episode and is narrative focused); and (4)~the episode's plot which is the largest part and summarizes the narrative developments of the episode. The plot sections are also usually divided into sub-sections aligned to narrative topics. 
% followed by a pre-show and announcements section where some non-narrative information and logistical information is mentioned. A 'Previously on Critical Role' follows, where the \emph{previous} episode of Critical Role is summarized. This is usually done by Matt in the episode and is usually very narrative focused. Following this is the episode's synopsis block 
% which is usually the bulk of all the summary text (mostly narrative focused). It is usually divided into multiple parts, each with its own sub-title. 
Sometimes the wiki also has a break and post-episode sections (usually non-narrative), which we include in the dataset.
\subsection{Analysis and Comparison}
% Each episode's transcript is a dialogue, with narrative ties to the previous and subsequent transcript.
Refer to Table~\ref{dataset statistics} for turn and token count comparisons.
% These 9 are the main cast (players) and the DM; together, they make up 350,324 turns out of the 390,543. The DM alone makes up 120,243 turns.
\crd's total turn count, turns per dialogue, and unique token count are substantially larger than MELD \cite{poria-etal-2019-meld} (scripted Friends TV show dataset), Multi-WOZ \cite{budzianowski-etal-2018-multiwoz} (unscripted task-oriented dialogue dataset), and AMI \cite{Carletta2005AMI} (unscripted meetings dataset). For AMI, we only consider the dialogues with available abstractive summaries \footnote{\href{https://github.com/gcunhase/AMICorpusXML}{github.com/gcunhase/AMICorpusXML}}. 
Multi-WOZ is dyadic while AMI, MELD, and \crd~have multiple speakers per dialogue.
% MELD's higher ratio of unique tokens to total tokens despite lower total turns than CRD3 and AMI (the other spoken dialogues) indicates higher information density than more naturally spoken dialogue.
% We also notice a substantial difference in the ratio of unique token count to token count for CRD3, Multi-WOZ, AMI, and MELD: 0.008, 0.012, 71, and 19 respectively.

We extract 72 total speakers from the entire \crd~dataset; 9 of which are the main cast (players and DM) and make up 99.48\% of the total turns; the DM alone makes up 111,994 turns. In comparison, the 6 main cast of MELD make up 83.27\% of the total turns.
% 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. 
In addition to real (human) speakers, there are also purely in-game characters role-played by the DM.
The indication of the DM role-playing through the use of quotes seem to be mostly consistent in the transcripts.
% In the raw transcripts, the transcribers seem to be mostly consistent with the indication of the DM role-playing through the use of quotes. 
As a loose measure of role-playing, we find the turns that contain quotes from the DM (\(\approx\)21383) and compare to all other players (\(\approx\)2497). 
A core aspect of the game is players querying the DM, so we also measure the instances of questions from a player (turn ending in `?') followed by a DM response; a mean of 199 per dialogue with 58 standard deviation.
Finally, we apply the spaCy English NER model on all dialogues as a loose measure of named entity presence. We get a mean of 1275 entities per dialogue with standard deviation of 344.5.
% Finally, we measure name mentions as a loose measure of named entity presence in the dialogues. Using the Spacy english NER model; a mean of 270.98 with std 77.4.% at an average of 60 tokens\textbf{}

For the summaries, we measure the token counts per summary and compare to AMI, CNN, and Daily Mail (Table \ref{dataset statistics}). Again, \crd~is substantially larger (though smaller in total tokens than the news datasets). The news datasets also feature more summary-article pairs, making them more amenable to current neural ML approaches; we address this for \crd~in Section~\ref{sec:augmentation}. We also measure the compression of the original text to summary via ratio of tokens per summary to tokens per original text and find they correspond to the ratios of total tokens to unique tokens.
Finally, we measure the average token count and standard deviation of each section of the structured summaries for the \crd~dataset (outlined in Section~\ref{sec:summary description}):
(1)~Wiki opening blurb: \(50\pm16.7\); (2)~pre-show and announcements: \(183\pm254\); (3)~recap: \(335\pm123.9\); and (4)~episode plot: \(1544\pm1553.7\).
% This is substantially larger than the AMI dialogue abstractive summaries, and larger than the CNN and DailyMail abstractive news summaries. There are also more abstractive summaries available in the CRD3 dataset than in the AMI dataset (but much fewer than the news summaries). 
% CNN has x582 more summaries, but only x12 more total token count; motivating the data augmentation approach described in section 4.
