diff --git a/gensim/models/doc2vec.py b/gensim/models/doc2vec.py
index 9812dc5ef4..eff6a6ed37 100644
--- a/gensim/models/doc2vec.py
+++ b/gensim/models/doc2vec.py
@@ -447,18 +447,13 @@ class Doc2Vec(BaseWordEmbeddingsModel):
         directly to query those embeddings in various ways. See the module level docstring for examples.
 
     docvecs : :class:`~gensim.models.keyedvectors.Doc2VecKeyedVectors`
-        This object contains the paragraph vectors. Remember that the only difference between this model and
-        :class:`~gensim.models.word2vec.Word2Vec` is that besides the word vectors we also include paragraph embeddings
-        to capture the paragraph.
+        This object contains the paragraph vectors learned from the training data. There will be one such vector
+        for each unique document tag supplied during training. They may be individually accessed using the tag
+        as an indexed-access key. For example, if one of the training documents used a tag of 'doc003':
 
-        In this way we can capture the difference between the same word used in a different context.
-        For example we now have a different representation of the word "leaves" in the following two sentences ::
-
-            1. Manos leaves the office every day at 18:00 to catch his train
-            2. This season is called Fall, because leaves fall from the trees.
+        .. sourcecode:: pycon
 
-        In a plain :class:`~gensim.models.word2vec.Word2Vec` model the word would have exactly the same representation
-        in both sentences, in :class:`~gensim.models.doc2vec.Doc2Vec` it will not.
+            >>> model.docvecs['doc003']
 
     vocabulary : :class:`~gensim.models.doc2vec.Doc2VecVocab`
         This object represents the vocabulary (sometimes called Dictionary in gensim) of the model.