diff --git a/slides/methods.tex b/slides/methods.tex
index 3457ccdb91813eae424d542f4292b442e12b2833..1828921b98e478533515792fb3453ba399d091da 100644
--- a/slides/methods.tex
+++ b/slides/methods.tex
@@ -3,19 +3,17 @@
 \begin{frame}
 	\frametitle{Similarity}
 	\begin{itemize}
-		\item  For the ranking of arguments, we measured the semantic similarity
-		      between premise and conclusion
-		\item Here each word of the argument in embedded in a vector space and the
-		      average of the vectors of the argument is calculated
-		\item The similarity of a premise and a conclusion is the calculated by the
-		      angle between them
+		\item For ranking the arguments, we measured the semantic similarity
+		      between the premises and conclusions
+		\item Each argument was embedded word-wise in an averaged vector space
+		\item The resulting similarity was calculated by using $cos(c, p)$
 		\item In the course of this experiment, we used three different embeddings
 		      \begin{itemize}
 			      \item BERT\footnote{J. Devlin, M. Chang, K. Lee, and K. Toutanova, “BERT: pre-training of deep bidirectional transformers
 for language understanding,”}
-			      \item Elmo\footnote{M. E. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, and L. Zettlemoyer, “Deep
+			      \item ELMo\footnote{M. E. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, and L. Zettlemoyer, “Deep
 contextualized word representations,”}
-			      \item Glove\footnote{J. Pennington, R. Socher, and C. Manning, “Glove: Global vectors for word representation,”}
+			      \item GloVe\footnote{J. Pennington, R. Socher, and C. Manning, “Glove: Global vectors for word representation,”}
 		      \end{itemize}
 	\end{itemize}
 \end{frame}
@@ -24,9 +22,8 @@ contextualized word representations,”}
 \begin{frame}
 	\frametitle{Sentiment}
 	\begin{itemize}
-		\item Another approach to rank the argument is to measure how positive the tone
-		      of the premises is
-		\item For this, we used a sentiment neural network based on FastText\footnote{A. Joulin, E. Grave, P. Bojanowski, and T. Mikolov, “Bag of tricks for efficient text classification,”}, which was
+		\item As another approach we used to measure the positivity of the argument
+		\item Therefore, we used a sentiment neural network based on FastText\footnote{A. Joulin, E. Grave, P. Bojanowski, and T. Mikolov, “Bag of tricks for efficient text classification,”}, which was
 		      trained on film ratings of IMDb
 	\end{itemize}
 \end{frame}
\ No newline at end of file