diff --git a/docs/docs/integrations/document_loaders/amazon_textract.ipynb b/docs/docs/integrations/document_loaders/amazon_textract.ipynb
index b76b6ddf630..71da1059ea1 100644
--- a/docs/docs/integrations/document_loaders/amazon_textract.ipynb
+++ b/docs/docs/integrations/document_loaders/amazon_textract.ipynb
@@ -11,11 +11,9 @@
     ">\n",
     ">It goes beyond simple optical character recognition (OCR) to identify, understand, and extract data from forms and tables. Today, many companies manually extract data from scanned documents such as PDFs, images, tables, and forms, or through simple OCR software that requires manual configuration (which often must be updated when the form changes). To overcome these manual and expensive processes, `Textract` uses ML to read and process any type of document, accurately extracting text, handwriting, tables, and other data with no manual effort. \n",
     "\n",
-    "This sample demonstrates the use of `Amazon Textract` in combination with LangChain as a DocumentLoader.\n",
+    "`Textract` supports `JPEG`, `PNG`, `PDF`, and `TIFF` file formats; more information is available in [the documentation](https://docs.aws.amazon.com/textract/latest/dg/limits-document.html).\n",
     "\n",
-    "`Textract` supports`PDF`, `TIFF`, `PNG` and `JPEG` format.\n",
-    "\n",
-    "`Textract` supports these [document sizes, languages and characters](https://docs.aws.amazon.com/textract/latest/dg/limits-document.html)."
+    "The following samples demonstrate the use of `Amazon Textract` in combination with LangChain as a DocumentLoader."
    ]
   },
   {
@@ -310,17 +308,6 @@
     "\n",
     "chain.run(input_documents=documents, question=query)"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bd97f1c90aff6a83",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "source": []
   }
  ],
  "metadata": {