diff --git a/slides/results.tex b/slides/results.tex index 93e25607373f83b33fbdb0365359dc3672989b00..9a5fccaae2affef99ebd834fea31fe7ce1fa9024 100644 --- a/slides/results.tex +++ b/slides/results.tex @@ -1,17 +1,57 @@ \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