Commit c6f16b0f authored by Marc Feger's avatar Marc Feger
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Refactor results.tex and add table

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\section{Results}
\begin{frame}
\begin{figure}
\frametitle{Evaluation of PageRank}
\centering
\includegraphics[width=\linewidth]{bilder/PageRankComparison.png}
\caption{Development of the perception regarding the argument relevance induced
by PageRank regarding all possible aggregations. CPR, NetworkX and NetworkX
using Scipy were plotted against by Wachsmuth et al. the result obtained for different $\alpha$ values, which regulates the influence of linking the arguments.}
\caption{Direct comparison of the different PageRank against the approach of \cite{wachsmuth:2017a}.}
\end{figure}
\end{frame}
\begin{frame}
\frametitle{Baseline Evaluation}
\begin{figure}
\centering
\includegraphics[width=\linewidth]{bilder/PairwiseResultComparison.png}
\caption{Direct comparison of all baseline values reported by Wachsmuth et al. with all results obtained in this paper.}
\caption{Direct comparison of all baseline values reported by \cite{wachsmuth:2017a} with all results obtained in this paper.}
\end{figure}
\end{frame}
\begin{frame}
\frametitle{Detailed Results}
\begin{table}[hbp!]
\centering
\resizebox{\textwidth}{!}{
\begin{tabular}{llrrrcrrrcrrrcrrrcrrr}
\toprule
\multirow{3}{*}{\textbf{\#}} &
\multirow{3}{*}{\textbf{Approach}} &
\multicolumn{3}{c}{\textbf{(a) Minimum}}& &
\multicolumn{3}{c}{\textbf{(b) Average}} & &
\multicolumn{3}{c}{\textbf{(c) Maximum}} & &
\multicolumn{3}{c}{\textbf{(d) Sum}} & &
\multicolumn{3}{c}{\textbf{(e) Best results}} \\
\cline{3-5} \cline{7-9} \cline{11-13} \cline{15-17} \cline{19-21} & & $\tau$ & \textit{best} & \textit{worst} & & $\tau$ & \textit{best} & \textit{worst} & & $\tau$ & \textit{best} & \textit{worst} & & $\tau$ & \textit{best} & \textit{worst} & & $\tau$ & \textit{best} & \textit{worst}\\
\midrule
\small 1 & PageRank & 0.01 & 8 & 6 & & 0.02 & 9 & 7 & & 0.11 & 8 & 6 & & 0.28 & 11 & 5 & & 0.28 & 11 & 5 \\
\small 2 & Frequency & -0.10 & 2 & 8 & & -0.03 & 3 & 9 & & -0.01 & 2 & 8 & & 0.10 & 6 & 8 & & 0.10 & 6 & 8 \\
\small 3 & Similarity & -0.13 & 4 & 11 & & -0.05 & 5 & 11 & & 0.01 & 6 & 10 & & 0.02 & 6 & 10 & & 0.02 & 6 & 10 \\
\small 4 & Sentiment & 0.01 & 6 & 7 & & 0.11 & 9 & 4 & & 0.12 & 6 & 4 & & 0.12 & 9 & 4 & & 0.12 & 9 & 4 \\
\small 5 & Most premises & n/a & n/a & n/a & & n/a & n/a & n/a & & n/a & n/a & n/a & & 0.19 & 3 & 3 & & 0.19 & 3 & 3 \\
\small 6 & Random & n/a & n/a & n/a & & n/a & n/a & n/a & & n/a & n/a & n/a & & 0.00 & 5 & 7 & & 0.00 & 5 & 7 \\
\midrule
\small 7 & SNN & 0.12 & 10 & 6 & & 0.24 & 11 & 5 & & 0.31 & 12 & 5 & & 0.30 & 13 & 5 & & 0.31 & 13 & 5 \\
\small 8 & GWP & \textbf{0.22} & 12 & 5 & & \textbf{0.28} & 13 & \textbf{3} & & \textbf{0.39} & \textbf{14} & \textbf{2} & & \textbf{0.47} & \textbf{16} & \textbf{1} & & \textbf{0.47} & \textbf{16} & \textbf{1} \\
\small 9 & GWOP & -0.06 & 5 & 9 & & 0.00 & 6 & 7 & & 0.14 & 8 & 6 & & 0.20 & 8 & 4 & & 0.20 & 8 & 4 \\
\small 10 & EWP & 0.03 & 6 & 9 & & 0.08 & 7 & 8 & & 0.11 & 8 & 8 & & 0.28 & 9 & 5 & & 0.28 & 9 & 5 \\
\small 11 & EWOP & -0.04 & 5 & 9 & & 0.03 & 6 & 8 & & 0.07 & 7 & 8 & & 0.23 & 9 & 6 & & 0.23 & 9 & 6 \\
\small 12 & BWP & -0.09 & 6 & 9 & & -0.02 & 7 & 8 & & 0.05 & 9 & 8 & & 0.24 & 10 & 5 & & 0.24 & 10 & 5 \\
\small 13 & BWOP & -0.06 & 6 & 9 & & -0.01 & 7 & 8 & & 0.07 & 9 & 8 & & 0.26 & 10 & 5 & & 0.26 & 10 & 5 \\
\small 14 & MKBM & 0.10 & 5 & 7 & & 0.08 & 13 & 6 & & 0.24 & 12 & 8 & & 0.34 & 11 & 9 & & 0.34 & 13 & 6 \\
\small 15 & AKBM & 0.15 & \textbf{14} & \textbf{4} & & 0.26 & \textbf{14} & 4 & & 0.38 & 11 & 7 & & 0.40 & 13 & 7 & & 0.40 & 14 & 4 \\
\bottomrule
\end{tabular}
}
\vspace*{0mm}
\caption{Comparison of the approaches of \cite{wachsmuth:2017a} (1-6) with those used in this study (7-15). For each aggregation (a-d) the average agreement $\tau$ and the cases in which the respective approach performed best or worst over the 32 conclusions of the 110 arguments are given. (e) shows the best results of an aggregation.}
\label{tab:results}
\end{table}
\end{frame}
\ No newline at end of file
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