% No results var.
\newcommand{\nr}[1]{--}
% Considered results.
\definecolor{Gray}{gray}{0.85}
 \newcolumntype{a}{>{\columncolor{Gray}}c}

\begin{table*}[!t]
  \centering
  \small
  \begin{tabular}{lccacaca}
  \hline
       & \multirowcell{3}{Input \\ version} & \multicolumn{2}{c}{C1} & \multicolumn{4}{c}{C2}   \\
    \cline{3-8} 
     Model       &                                 & \devSet{}    &  \testSetCOne{}    & \multicolumn{4}{c}{ \testSetCTwo{} \cellcolor{Gray}}   \\
    \cline{3-8} 
                                                   &                       & F1                 & F1                & Rouge1           & Rouge2    & RougeL     & F1 \\
    \hline       
    % SVM (RBF)                                                   & \textsc{orig}                 & 56.64              & \nr{}             &   \nr{}      & \nr{} & \nr{}  & \nr{}     \\ 
    % SVM (RBF)                                                   & v1                    & 54.82              & \nr{}             &  \nr{}      &  \nr{}     &  \nr{}  & \nr{}    \\ 
    % RandomForest                                                & \textsc{orig}                 & \textbf{59.45}     & \nr{}             &   \nr{}      & %\nr{} & \nr{}  & \nr{}     \\ 
    % RandomForest                                                & v1                    & 58.75              & \nr{}                & \nr{}        & \nr{} % & \nr{}   & \nr{}     \\  \hline
    %BasicLSTM 2k\textsubscript{words}                          & v1                    & 49.79              & \nr{}             &   \nr{}      & \nr{} & \nr{} & \nr{}      \\ 
    %BasicLSTM 4k\textsubscript{words}                          & v1                    & 55.35              & \nr{}             &   \nr{}      & \nr{} & \nr{} & \nr{}      \\ 
    %RCNN + PZhou 2K\textsubscript{words}                       & v1                    & 56.67              & \nr{}             &   \nr{}      & \nr{} & \nr{} & \nr{}      \\ 
    %RCNN + PZhou 4K\textsubscript{words}                       & v1                    & 59.38              & \nr{}             &   \nr{}      & \nr{} & \nr{} & \nr{}      \\ 
    % \hline
    CNN\textsubscript{f=300}                              & v1                    & 63.30              & \nr{}             &   \nr{}      & \nr{} & \nr{} & \nr{}      \\ 
    CNN\textsubscript{f=600}                              & v1                    & \textbf{66.13}     & 57.16             &   \nr{}      & \nr{} & \nr{} & \nr{}      \\ 
    CNN\textsubscript{f=600}                              & v2, $K=100\%$         & \nr{}              & \textbf{60.09}    &   \nr{}      & \nr{} & \nr{} & \nr{}      \\ 
    CNN\textsubscript{f=600}                              & v2, $K=75\%$          & \nr{}              & 58.74             &   \nr{}      & \nr{} & \nr{} & \nr{}      \\ 
    CNN\textsubscript{f=1200}                              & v1                    & 65.50              & \nr{}            &   \nr{}      & \nr{} & \nr{} & \nr{}      \\ 
    %CNN (600f) 4K\textsubscript{words}                         & rm-dup+rev+so-ord     & \nr{}              & 58.57             &   \nr{}      & \nr{} & \nr{} & \nr{}      \\ 
    \hline
    AttCNN\textsubscript{f=600}                           & v1                    &         57.96      & \nr{}             &   21.20      & 4.50 & 18.10 & \textbf{68.54}     \\ 
    AttCNN\textsubscript{f=600}                           & v2, $K=100\%$     &         \nr{}      & \nr{}             &   20.89      & 4.60 & 17.35 & 42.58      \\ 
    AttCNN\textsubscript{f=600}                           & v2, $K=75\%$      &         \nr{}      & \nr{}             &   21.46      & \textbf{4.65} & 18.31 & 47.77      \\  \hline
    \hline
    %RoBERTa 255\textsubscript{begin+end-tokens}          
    %                          & orig.                    &          62.54     &  \nr{}            &   \nr{}      & \nr{} & \nr{} & \nr{}      \\ 
    RoBERTa                                                  & \textsc{orig}              &          65.81     & 57.26             &   22.39      & 4.52 & 19.14 & 50.00       \\ 
    RoBERTa                                                  & v1                    &  \textbf{66.18}    &  58.53            &   22.39      & \textbf{4.54} & 19.04 & \textbf{52.68}      \\ 
    %Longformer (Last 1K\textsubscript{tokens})                 & orig.              &         65.27      & \nr{}             &   \nr{}      & \nr{} & \nr{} & \nr{}      \\ 
    \cline{2-8}
    Longformer\textsubscript{base}                              & \textsc{orig}  & 64.64 & \nr{} & \nr{}  & \nr{}  & \nr{}  & \nr{} \\
    Longformer\textsubscript{base}                                               & v1                    &         65.40     & 55.90              &   \nr{}      & \nr{} & \nr{} & \nr{}      \\ 
    \cline{2-8}
    %Longformer                                                 & v2                    &        --         &                    &   \nr{}      & \nr{} & \nr{} & \nr{}      \\ 
    %LegalBERT\textsubscript{base}                                       & v1                    &         64.58     &        ?           &   \nr{}      &  \nr{}  & \nr{}      & \nr{}           \\ 
    LegalBERT\textsubscript{base}                                        & v1                    &         65.82     &  \textbf{63.51}    &   21.33      &  4.15 & 18.39      &  50.00          \\ 
    \hline
    \hline
    \textbf{Final Submission}                                           &                                   &             65.82     &  \textbf{63.51}     &      21.46      & \textbf{4.65} & 18.31 & 47.77       \\ 
    \hline
  \end{tabular}
  \caption{Results of the single run in tasks C1 and C2; all the language models are \textsc{BASE} sized; gray column highights the results were mentioned in official leaderboard during evaluation stage; the highest results mentioned in leaderboard per every block of models are bolded; 
  $K$ corresponds to the most frequent words selection of \textbf{v2} shortening;
  all the results presented in percents (multiplied by 100)}
  \label{tab:results}
\end{table*}
