RiteeshGrandhi/sciclaimeval-shared-task huggingface dataset:
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth}
  \begin{tabular}{lcccccc}
    \toprule
    \multirow{2}{*}{Method} &\multicolumn{2}{c}{QAG} &\multicolumn{2}{c}{XSum-H} &\multirow{2}{*}{Podcast} &\multirow{2}{*}{SumEvl} \\
     &CNNDM &XSum &Faithful &Factual  &  \\
    \midrule
    \rowcolor{Gray}
    \multicolumn{7}{l}{\textit{Baselines: Other Approaches}} \\
    ROUGE-1                  &0.337  &0.012 &-0.050 &0.008  &0.326  &0.458  \\
    OpenIE-TripleMatching    &0.381  &0.131 &0.019  &-0.020 &0.706  &0.548  \\
    BERTScore                &0.584  &0.008 &0.185  &0.154  &0.718  &0.645  \\
    Entailment (BERT Model)  &0.159  &0.169 &0.362  &0.209  &0.228  &0.619  \\
    \rowcolor{Gray}
    \multicolumn{7}{l}{\textit{Baselines: SpanQAG}} \\
    QAGS                     &0.437  &0.200 &0.101  &0.080  &0.464  &0.812  \\
    FEQA                     &0.322  &0.283 &0.297  &0.171  &0.603  &0.464  \\
    QuestEval                &0.250  &0.173 &0.421  &0.197  &0.579  &0.838  \\
    \rowcolor{Gray}
    \multicolumn{7}{l}{\textbf{Multiple-choice Question Answering and Generation (MQAG)}} \\
    MQAG$_\text{SQuAD}$   &\underline{0.519} &\underline{0.407} &0.324 &\underline{0.292} &0.502 &\underline{0.890} \\
    MQAG$_\text{RACE}$    &\underline{0.502} &\underline{0.313} &0.306 &\underline{0.270} &\underline{0.855} &\underline{0.945} \\
    \bottomrule
  \end{tabular}}
\end{adjustbox}
\caption{Table 5: Pearson Correlation Coefficient (PCC) between the scores of summary evaluation methods and human judgements. PCCs are computed at the summary level on QAG and XSum-H, and at the system level on Podcast and SummEval. PCCs on Podcast are computed on 15 abstractive systems. Our best performing MQAG configuration consists of (i) generation stage {\tt G} generates questions from summary $y$ (i.e. MQAG-Sum), (ii) statistical distance is total variation, (iii) the answerability threshold $\mathcal{N}^{\tau}_y$ is set to 2.0. Underline denotes where MQAG outperforms the best SpanQAG system, which is 5 out of 6 tasks. When compared to all baselines, MQAG achieves the highest PCC on 4 out of 6 tasks. The results of all MQAG configurations are provided in Table~tab:mqag:pearson:appendix, and Spearman's correlation results are provided in Table~tab:baseline:vs:mqag:spearman in the appendix.}
\end{table}