diff --git a/README.rst b/README.rst
index 1f8d7e0..6ba6d73 100644
--- a/README.rst
+++ b/README.rst
@@ -129,6 +129,22 @@ start using it for talon.
 .. _EDRM: http://www.edrm.net/resources/data-sets/edrm-enron-email-data-set
 .. _forge: https://github.com/mailgun/forge
 
+Training on your dataset
+------------------------
+
+talon comes with a pre-processed dataset and a pre-trained classifier. To retrain the classifier on your own dataset of raw emails, structure and annotate them in the same way the `forge`_ project does. Then do:
+
+.. code:: python
+
+    from talon.signature.learning.dataset import build_extraction_dataset
+    from talon.signature.learning import classifier as c 
+    
+    build_extraction_dataset("/path/to/your/P/folder", "/path/to/talon/signature/data/train.data")
+    c.train(c.init(), "/path/to/talon/signature/data/train.data", "/path/to/talon/signature/data/classifier")
+
+Note that for signature extraction you need just the folder with the positive samples with annotated signature lines (P folder).
+
+.. _forge: https://github.com/mailgun/forge
 
 Research
 --------
diff --git a/talon/quotations.py b/talon/quotations.py
index 0fecf8a..8b368e5 100644
--- a/talon/quotations.py
+++ b/talon/quotations.py
@@ -434,6 +434,9 @@ def _extract_from_html(msg_body):
     Extract not quoted message from provided html message body
     using tags and plain text algorithm.
 
+    Cut out first some encoding html tags such as xml and doctype
+    for avoiding conflict with unicode decoding
+
     Cut out the 'blockquote', 'gmail_quote' tags.
     Cut Microsoft quotations.
 
@@ -449,6 +452,9 @@ def _extract_from_html(msg_body):
         return msg_body
 
     msg_body = msg_body.replace(b'\r\n', b'\n')
+
+    msg_body = re.sub(r"\<\?xml.+\?\>|\<\!DOCTYPE.+]\>", "", msg_body)
+
     html_tree = html_document_fromstring(msg_body)
 
     if html_tree is None: