diff --git a/tensorflow/lite/g3doc/r2/convert/python_api.md b/tensorflow/lite/g3doc/r2/convert/python_api.md
index b5ced832960..882346fb8cf 100644
--- a/tensorflow/lite/g3doc/r2/convert/python_api.md
+++ b/tensorflow/lite/g3doc/r2/convert/python_api.md
@@ -150,7 +150,20 @@ output_data = interpreter.get_tensor(output_details[0]['index'])
 print(output_data)
 ```
 
-## Summary of changes in TFLiteConverter in 1.X and 2.0 <a name="differences"></a>
+## Summary of changes in `TFLiteConverter` between 1.X and 2.0 <a name="differences"></a>
+
+The following section summarizes the changes in `TFLiteConverter` from 1.X to
+2.0. If any of the changes raise concerns, please file a
+[GitHub issue](https://github.com/tensorflow/tensorflow/issues).
+
+### Supported formats
+
+`TFLiteConverter` in 2.0 supports SavedModels and Keras model files generated in
+both 1.X and 2.0. However, the conversion process no longer supports frozen
+`GraphDefs` generated in 1.X. Users who want to convert frozen `GraphDefs` to
+TensorFlow Lite should use `tensorflow.compat.v1`.
+
+### Quantization-aware training
 
 The following attributes and methods associated with
 [quantization-aware training](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/quantize)
@@ -173,6 +186,8 @@ removed in the 2.0 API until the new quantization API is launched. Users who
 want to convert models generated by the rewriter function can use
 `tensorflow.compat.v1`.
 
+### Changes to attributes
+
 The `target_ops` attribute has become an attribute of `TargetSpec` and renamed
 to `supported_ops` in line with future additions to the optimization framework.
 
@@ -190,15 +205,14 @@ Additionally, the following attributes have been removed:
     *   `dump_graphviz_dir`
     *   `dump_graphviz_video`
 
+### Deprecated APIs
+
 The following methods that were previously deprecated in 1.X will no longer be
 exported in 2.0:
 
 *   `lite.toco_convert`
 *   `lite.TocoConverter`
 
-If any of the changes raise concerns, please file a
-[GitHub issue](https://github.com/tensorflow/tensorflow/issues).
-
 ## Installing TensorFlow <a name="versioning"></a>
 
 ### Installing the TensorFlow 2.0 nightly <a name="2.0-nightly"></a>