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\begin{table}
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\toprule
\multirow{2}{*}{Method} &\multicolumn{2}{c}{QAG} &\multicolumn{2}{c}{XSum-H} &\multirow{2}{*}{Podcast} &\multirow{2}{*}{SumEvl} \\
&CNNDM &XSum &Faithful &Factual & \\
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\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 \\
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\multicolumn{7}{l}{\textbf{Multiple-choice Question Answering and Generation (MQAG)}} \\
MQAG$_\text{SQuAD}$ &\underline{0.219} &\underline{0.107} &0.124 &\underline{0.192} &0.502 &\underline{0.190} \\
MQAG$_\text{RACE}$ &\underline{0.102} &\underline{0.113} &0.106 &\underline{0.170} &\underline{0.155} &\underline{0.145} \\
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\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.}
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