Minor formatting and additional figure for summarization use case (#8663)

docs/extras/use_cases/summarization.ipynb

@@ -11,8 +11,6 @@
     "\n",
     "## Use case\n",
     "\n",
-    "--- \n",
-    "\n",
     "Suppose you have a set of documents (PDFs, Notion pages, customer questions, etc.) and you want to summarize the content. \n",
     "\n",
     "LLMs are a great tool for this given their proficiency in understanding and synthesizing text.\n",
@@ -37,8 +35,6 @@
    "source": [
     "## Overview\n",
     "\n",
-    "--- \n",
-    "\n",
     "A central question for building a summarizer is how to pass your documents into the LLM's context window. Two common approaches for this are:\n",
     "\n",
     "1. `Stuff`: Simply \"stuff\" all your documents into a single prompt. This is the simplest approach (see [here](/docs/modules/chains/document/stuff) for more on the `StuffDocumentsChains`, which is used for this method).\n",
@@ -61,8 +57,6 @@
    "source": [
     "## Quickstart\n",
     "\n",
-    "--- \n",
-    "\n",
     "To give you a sneak preview, either pipeline can be wrapped in a single object: `load_summarize_chain`. \n",
     "\n",
     "Suppose we want to summarize a blog post. We can create this in a few lines of code.\n",
@@ -136,8 +130,6 @@
    "source": [
     "## Option 1. Stuff\n",
     "\n",
-    "--- \n",
-    "\n",
     "When we use `load_summarize_chain` with `chain_type=\"stuff\"`, we will use the [StuffDocumentsChain](/docs/modules/chains/document/stuff).\n",
     "\n",
     "The chain will take a list of documents, inserts them all into a prompt, and passes that prompt to an LLM:"
@@ -201,8 +193,6 @@
    "source": [
     "## Option 2. Map-Reduce\n",
     "\n",
-    "---\n",
-    "\n",
     "Let's unpack the map reduce approach. For this, we'll first map each document to an individual summary using an `LLMChain`. Then we'll use a `ReduceDocumentsChain` to combine those summaries into a single global summary.\n",
     " \n",
     "First, we specfy the LLMChain to use for mapping each document to an individual summary:"
@@ -361,7 +351,11 @@
     "\n",
     "**Real-world use-case**\n",
     "\n",
-    "* See [this blog post](https://blog.langchain.dev/llms-to-improve-documentation/) case-study on analyzing user interactions (questions about LangChain documentation)!  "
+    "* See [this blog post](https://blog.langchain.dev/llms-to-improve-documentation/) case-study on analyzing user interactions (questions about LangChain documentation)!  \n",
+    "* The blog post and associated [repo](https://github.com/mendableai/QA_clustering) also introduce clustering as a means of summarization.\n",
+    "* This opens up a third path beyond the `stuff` or `map-reduce` approaches that is worth considering.\n",
+    "\n",
+    "![Image description](/img/summarization_use_case_3.png)"
    ]
   },
   {
@@ -371,8 +365,6 @@
    "source": [
     "## Option 3. Refine\n",
     " \n",
-    "--- \n",
-    " \n",
     "[Refine](/docs/modules/chains/document/refine) is similar to map-reduce:\n",
     "\n",
     "> The refine documents chain constructs a response by looping over the input documents and iteratively updating its answer. For each document, it passes all non-document inputs, the current document, and the latest intermediate answer to an LLM chain to get a new answer.\n",
@@ -497,9 +489,9 @@
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@@ -511,7 +503,7 @@
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