\begin{table*}[t!]
\centering
\small
\begin{tabular}{l | r | rrr | rr}
\hline
\textbf{Metric} & \textbf{CRD3}& \textbf{MELD} & \textbf{M. WOZ} & \textbf{AMI} & \textbf{CNN}& \textbf{DailyMail}\\
\hline
Dialogue Count & 159 & 190 & 10438 & 142 & 92465 & 219506 \\
Turn Count & 398682 & 13708 & 143048 & 79672 & 3074340 & 6189038\\
Total token count in dialogues & 5056647 & 120913 & 1886018 & 706803 & 60476397 & 154282948\\
Unique token count in dialogues & 42509 & 6251 & 20197 & 9958 & 341451 & 596032\\
Avg. turns per dialogue & 2507.4 & 72.2 & 13.7 & 561.1 & 33.4 & 28.2 \\
Avg. tokens per turn & 12.7 & 8.82 & 13.2 & 8.9 & 19.7 & 24.9\\
Total token count in summaries & 327899 &-&-& 22965 & 3897045 & 11308821\\
Avg. tokens per summary & 2062.3 &- &- & 161.7 & 42.1 & 51.5\\
Avg. summary:dialogue token ratio& 0.065 &- &- & 0.038 & 0.085 & 0.087\\
\hline
\end{tabular}
\caption{\label{dataset statistics}
We compare \crd~with other similar datasets. MELD, Multi-WOZ, and AMI are dialogue datasets. We use the subset of the AMI dialogues with available abstractive summaries. CNN and Daily Mail are abstractive summarization datasets for news articles (we treat an article as a dialogue and a sentence as a turn).
}
\end{table*}