diff --git a/abstract.tex b/abstract.tex
index 62090f4..e193e2f 100644
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+++ b/abstract.tex
@@ -3,12 +3,14 @@
 
 Object detection in open set conditions is an important part of
 applying object detection to real world data sets. The key question
-is how to identify unknown or novel data. This thesis compares
-vanilla SSD and SSD with dropout sampling on the MS COCO data set.
-The hyper parameters confidence threshold, entropy threshold,
-usage of non-maximum suppression, and usage of dropout layers during
-testing are varied to create different scenarios. Both results from macro and
-micro averaging are presented.
+is how to identify unknown or novel data. Dropout sampling with entropy
+thresholding is one possible avenue to answer the question.
+
+This thesis compares vanilla SSD and SSD with dropout sampling on the MS COCO
+data set. The hyper parameters confidence threshold, entropy threshold,
+usage of non-maximum suppression, usage of dropout layers during
+testing, and the dropout keep ratio are varied to create different scenarios.
+Both results from macro and micro averaging are presented.
 
 Results show a significant improvement of the object detection performance
 with a higher confidence threshold. The usage of non-maximum suppression