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diff --git a/slides/methods.tex b/slides/methods.tex
index f44bf14ff52c9d89e28bf5443a9da81c9c61c346..a192b8d6b52bfe107f21aa6142dce6af0930d737 100644
--- a/slides/methods.tex
+++ b/slides/methods.tex
@@ -4,64 +4,50 @@
 \begin{frame}
 	\frametitle{PageRank}
 	\begin{columns}
-		\column{0.6\textwidth}
+		\column{0.5\textwidth}
 		\begin{itemize}
-			\item PageRank by \cite{PageRank1999} was originally used to evaluate relevant websites via their links
-			\item Websites can be replaced by arguments
-			\item Linking results due to the reuse of conclusions and premises
-			\item In this work it was used:
-			\begin{itemize}
-				\item Custom-made PageRank
-				\item NetworkX PageRank
-				\item NetworkX-Scipy PageRank
-			\end{itemize}
+			\item PageRank originally used for websites
+			\item Websites replaced by arguments
 		\end{itemize}
 		\begin{block}{Custom-made PageRank}
 			\begin{equation*}
 				p_t(c_i) = \left\{
 				\begin{array}{lr}
-						(1 - \alpha)\frac{1}{|D|} + \alpha \sum_{j}{\frac{p_{t-1}(c_j)}{|P'_j|}} & : t > 0\\
-					\frac{1}{|D|} & : t = 0
+						(1 - \alpha)G_{rel} + \alpha L_{rel} & : t > 0\\
+					G_{rel} & : t = 0
 				  \end{array}
 			\right.
 			\end{equation*}
 		\end{block}
-		\column{0.4\textwidth}
+		\column{0.5\textwidth}
 			\includegraphics[scale=0.25]{bilder/ExampleGraph.png}
 		\end{columns}
 \end{frame}
 \begin{frame}
 	\frametitle{WordNet}
-	\begin{itemize}
-		\item As knowledge based method $Sim(T_1, T_2)$ of \cite{Mihalcea2006similarity} was used
-		\item $Sim(T_1, T_2)$ determines the semantic similarity of $T_1,T_2$ by mutually picking up highly similar concepts
-		\item The concepts were determined via WordNet
-		\item For $maxSim$ the $CoSim$ of \cite{Wu1994distance} was used
-	\end{itemize}
-	\begin{block}{$Sim(T_1, T_2)$}
-		\begin{equation*}
-			\frac{1}{2}(\frac{\sum_{w \in {T_1}}{maxSim(w, T_2)\cdot idf(w)}}{\sum_{w \in {T_1}}{idf(w)}}+\frac{\sum_{w \in {T_2}}{maxSim(w, T_1)\cdot idf(w)}}{\sum_{w \in {T_2}}{idf(w)}})
-		\end{equation*}
-	\end{block}
-	\begin{itemize}
-		\item Analogously, the average conceptual similarity between $T_1$ and $T_2$ was used as a weakened variant
-	\end{itemize}
+	\begin{columns}
+		\column{0.5\textwidth}
+		\begin{itemize}
+			\item Knowledge-based method
+			\item Conceptual similarity between conclusion and premise
+		\end{itemize}
+		\column{0.5\textwidth}
+			\includegraphics[scale=0.33]{bilder/Wordnet.png}
+		\end{columns}
 \end{frame}
 \begin{frame}
 	\frametitle{Similarity}
-	\begin{itemize}
-		\item For ranking the arguments, we measured the semantic similarity
-		      between the premises and conclusions
-		\item Argument were embedded word-wise in an averaged vector space
-		\item The resulting similarity was calculated by using $Cos(c, p)$
-	\end{itemize}
-	\begin{block}{Embeddings used}
-		BERT by \cite{devlin2018bert}
-
-		ELMo by \cite{Peters:2018ELMo}
-
-		GloVe by \cite{pennington2014glove}
-	\end{block}
+	\begin{columns}
+		\column{0.5\textwidth}
+		\begin{itemize}
+			\item Semantic similarity
+			\item Different embeddings
+			\item BERT, ELMo and GloVe
+			\item Similarity over $Cos(C, P)$
+		\end{itemize}
+		\column{0.5\textwidth}
+			\includegraphics[scale=0.4]{bilder/Vectors.png}
+	\end{columns}
 \end{frame}
 \begin{frame}
 	\frametitle{Sentiment}