diff --git a/docs/source/model_doc/encoderdecoder.rst b/docs/source/model_doc/encoderdecoder.rst
index a63b6044a..54f74320a 100644
--- a/docs/source/model_doc/encoderdecoder.rst
+++ b/docs/source/model_doc/encoderdecoder.rst
@@ -7,7 +7,7 @@ The effectiveness of initializing sequence-to-sequence models with pre-trained c
 
 After such an :class:`~transformers.EncoderDecoderModel` has been trained / fine-tuned, it can be saved / loaded just like any other models (see Examples for more information).
 
-An application of this architecture could be to leverage two pre-trained :obj:`transformers.BertModel` models as the encoder and decoder for a summarization model as was shown in: `Text Summarization with Pretrained Encoders <https://arxiv.org/abs/1910.13461>`_ by Yang Liu and Mirella Lapata. 
+An application of this architecture could be to leverage two pre-trained :obj:`transformers.BertModel` models as the encoder and decoder for a summarization model as was shown in: `Text Summarization with Pretrained Encoders <https://arxiv.org/abs/1908.08345>`_ by Yang Liu and Mirella Lapata. 
 
 
 ``EncoderDecoderConfig``