diff --git a/README.md b/README.md
index 85d89e691..3d23ce94a 100644
--- a/README.md
+++ b/README.md
@@ -15,19 +15,16 @@ Distributed Version: [Distributed XGBoost](multi-node)
 
 Notes on the Code: [Code Guide](src)
 
-Turorial and Documentation: https://github.com/dmlc/xgboost/wiki
-
-Video tutorial: [Better Optimization with Repeated Cross Validation and the XGBoost model - Machine Learning with R](https://www.youtube.com/watch?v=Og7CGAfSr_Y)
+Documentation: https://github.com/dmlc/xgboost/doc
 
 Learning about the model: [Introduction to Boosted Trees](http://homes.cs.washington.edu/~tqchen/pdf/BoostedTree.pdf)
 * This slide is made by Tianqi Chen to introduce gradient boosting in a statistical view.
 * It present boosted tree learning as formal functional space optimization of defined objective.
 * The model presented is used by xgboost for boosted trees
 
-Presention of a real use case of XGBoost to prepare tax audit in France: [Feature Importance Analysis with XGBoost in Tax audit](http://fr.slideshare.net/MichaelBENESTY/feature-importance-analysis-with-xgboost-in-tax-audit)
-
 What's New
 ==========
+* [External Memory Version](doc/external_memory.md)
 * XGBoost wins [WWW2015  Microsoft Malware Classification Challenge (BIG 2015)](http://www.kaggle.com/c/malware-classification/forums/t/13490/say-no-to-overfitting-approaches-sharing)
 * XGBoost now support HDFS and S3
 * [Distributed XGBoost now runs on YARN](https://github.com/dmlc/wormhole/tree/master/learn/xgboost)!
diff --git a/doc/README.md b/doc/README.md
index 801e7e65a..18d15ba9c 100644
--- a/doc/README.md
+++ b/doc/README.md
@@ -1,6 +1,6 @@
 XGBoost Documentation
 ====
-This is an ongoing effort to move the [wiki document](https://github.com/dmlc/xgboost/wiki) to here.
+This is an ongoing effort to move the [wiki document](https://github.com/dmlc/xgboost/wiki) to here. You can already find all the most useful parts here.
 
 List of Documentations
 ====
@@ -13,7 +13,9 @@ Highlights Links
 This section is about blogposts, presentation and videos discussing how to use xgboost to solve your interesting problem. If you think something belongs to here, send a pull request.
 * Blogpost by phunther: [Winning solution of Kaggle Higgs competition: what a single model can do](http://no2147483647.wordpress.com/2014/09/17/winning-solution-of-kaggle-higgs-competition-what-a-single-model-can-do/) 
 * [Kaggle Tradeshift winning solution by daxiongshu](https://github.com/daxiongshu/kaggle-tradeshift-winning-solution) 
+* Video tutorial: [Better Optimization with Repeated Cross Validation and the XGBoost model - Machine Learning with R](https://www.youtube.com/watch?v=Og7CGAfSr_Y)
+* Presention of a real use case of XGBoost to prepare tax audit in France: [Feature Importance Analysis with XGBoost in Tax audit](http://fr.slideshare.net/MichaelBENESTY/feature-importance-analysis-with-xgboost-in-tax-audit)
 
 Contribution
 ====
-Contribution of document usecases are welcomed!
+Contribution of document and usecases are welcomed!
diff --git a/doc/external_memory.md b/doc/external_memory.md
index e98133467..0a5d66ac4 100644
--- a/doc/external_memory.md
+++ b/doc/external_memory.md
@@ -1,4 +1,4 @@
-Using XGBoost External Memory Version
+Using XGBoost External Memory Version(beta)
 ====
 There is no big difference between using external memory version and in-memory version.
 The only difference is the filename format.