diff --git a/README.md b/README.md
index dc3d83c..2c16f87 100644
--- a/README.md
+++ b/README.md
@@ -5,11 +5,8 @@ Hoocs implements a broad range of model-agnostic attributions.
 - Shapley values [[3]]([3])
 - KernelSHAP [[4]]([4])
 Recently, there has been increasing interest in more in-depth analysis of models. To meet this needs, the analysis of 
-feature interactions is inevitable. Therefore, this package allows to calculate arbitrary higher-order explanations.
-
-Importantly, it is easily extendable to other methods, which rely on marginalizing features in input space. 
-This package puts a special focus on the on-manifoldness of attributions methods. To this end, we enable simple 
-incorporation of new conditional distributions (*imputers*) for any kind of data modality. 
+feature interactions is inevitable. Therefore, this package allows to calculate arbitrary higher-order explanations. 
+Tt is extendable to other methods, which rely on marginalizing features in input space.
 
 ## Installation
 ```
@@ -17,9 +14,8 @@ pip install hoocs
 ```
 
 ## Implement new imputers
-To add a new imputer to incorporate a suitable conditional distribution for the current data modality, 
-the user is requested to inherent from the abstract base `Imputer` class 
-in `hoocs.imputers.abstract_imputers.py`.
+To enable reliable attributions, hooks enables simple incorporation of custom *imputers* for any kind of data modality.
+To add a new imputer, the user is requested to inherent from the abstract base `Imputer` class in `hoocs.imputers.abstract_imputers.py`.
 This class performs basic type checking and ensures a consistent interface. 
 
 ## References