diff --git a/summary/main.tex b/summary/main.tex
index 363196d..62a46bc 100644
--- a/summary/main.tex
+++ b/summary/main.tex
@@ -98,6 +98,12 @@ Labeling datasets is commonly seen as an expensive task and wants to be avoided
 Thats why there is a machine-learning field called Semi-Supervised learning.
 The general approach is to train a model that predicts Pseudo-Labels which then can be used to train the main model.
 
+The goal of this paper is a video action recognition.
+Given are approximately 10 seconds long videos which should be classified.
+In this paper datasets with 400 and 101 different classes are used.
+The papers approach is tested with 1\% and 10\% of known labels of all data points.
+The used model depends on the exact usecase but in this case a 3D-ResNet50 and 3D-ResNet18 are used.
+
 \section{Semi-Supervised learning}\label{sec:semi-supervised-learning}
 In traditional supervised learning we have a labeled dataset.
 Each datapoint is associated with a corresponding target label.
@@ -208,7 +214,7 @@ And as a backbone model a 3D-ResNet18 and 3D-ResNet50 are used.
 \end{figure}
 
 \section{Further schemes}\label{sec:further-schemes}
-How the pseudo-labels are generated my impact the overall performance.
+How the pseudo-labels are generated may impact the overall performance.
 In this paper the pseudo-labels are obtained by the cross-model approach.
 But there might be other strategies.
 For example: