diff --git a/docs/notebooks/Corpora_and_Vector_Spaces.ipynb b/docs/notebooks/Corpora_and_Vector_Spaces.ipynb
index 57e7cf2238..b1eb3ce7db 100644
--- a/docs/notebooks/Corpora_and_Vector_Spaces.ipynb
+++ b/docs/notebooks/Corpora_and_Vector_Spaces.ipynb
@@ -340,7 +340,7 @@
    "source": [
     "Although the output is the same as for the plain Python list, the corpus is now much more memory friendly, because at most one vector resides in RAM at a time. Your corpus can now be as large as you want.\n",
     "\n",
-    "Similarly, to construct the dictionary without loading all texts into memory:"
+    "We are going to create the dictionary from the mycorpus.txt file without loading the entire file into memory. Then, we will generate the list of token ids to remove from this dictionary by querying the dictionary for the token ids of the stop words, and by querying the document frequencies dictionary (dictionary.dfs) for token ids that only appear once. Finally, we will filter these token ids out of our dictionary and call dictionary.compactify() to remove the gaps in the token id series."
    ]
   },
   {