[DOCS] Add overview page to analysis topic (#50515)

Adds a 'text analysis overview' page to the analysis topic docs.

The goals of this page are:

* Concisely summarize the analysis process while avoiding in-depth concepts, tutorials, or API examples
* Explain why analysis is important, largely through highlighting problems with full-text searches missing analysis
* Highlight how analysis can be used to improve search results

docs/reference/analysis.asciidoc

@@ -1,11 +1,11 @@
 [[analysis]]
-= Analysis
+= Text analysis
 
 [partintro]
 --
 
-_Analysis_ is the process of converting text, like the body of any email, into
-_tokens_ or _terms_ which are added to the inverted index for searching.
+_Text analysis_ is the process of converting text, like the body of any email,
+into _tokens_ or _terms_ which are added to the inverted index for searching.
 Analysis is performed by an <<analysis-analyzers,_analyzer_>> which can be
 either a built-in analyzer or a <<analysis-custom-analyzer,`custom`>> analyzer
 defined per index.
@@ -142,6 +142,8 @@ looking for:
 
 --
 
+include::analysis/overview.asciidoc[]
+
 include::analysis/anatomy.asciidoc[]
 
 include::analysis/testing.asciidoc[]

docs/reference/analysis/overview.asciidoc

@@ -0,0 +1,78 @@
+
+== Text analysis overview
+++++
+<titleabbrev>Overview</titleabbrev>
+++++
+
+Text analysis enables {es} to perform full-text search, where the search returns
+all _relevant_ results rather than just exact matches.
+
+If you search for `Quick fox jumps`, you probably want the document that
+contains `A quick brown fox jumps over the lazy dog`, and you might also want
+documents that contain related words like `fast fox` or `foxes leap`.
+
+[discrete]
+[[tokenization]]
+=== Tokenization
+
+Analysis makes full-text search possible through _tokenization_: breaking a text
+down into smaller chunks, called _tokens_. In most cases, these tokens are
+individual words.
+
+If you index the phrase `the quick brown fox jumps` as a single string and the
+user searches for `quick fox`, it isn't considered a match. However, if you
+tokenize the phrase and index each word separately, the terms in the query
+string can be looked up individually. This means they can be matched by searches
+for `quick fox`, `fox brown`, or other variations.
+
+[discrete]
+[[normalization]]
+=== Normalization
+
+Tokenization enables matching on individual terms, but each token is still
+matched literally. This means:
+
+*  A search for `Quick` would not match `quick`, even though you likely want
+either term to match the other
+
+* Although `fox` and `foxes` share the same root word, a search for `foxes`
+would not match `fox` or vice versa.
+
+* A search for `jumps` would not match `leaps`. While they don't share a root
+word, they are synonyms and have a similar meaning.
+
+To solve these problems, text analysis can _normalize_ these tokens into a
+standard format. This allows you to match tokens that are not exactly the same
+as the search terms, but similar enough to still be relevant. For example:
+
+* `Quick` can be lowercased: `quick`.
+
+* `foxes` can be _stemmed_, or reduced to its root word: `fox`.
+
+* `jump` and `leap` are synonyms and can be indexed as a single word: `jump`.
+
+To ensure search terms match these words as intended, you can apply the same
+tokenization and normalization rules to the query string. For example, a search
+for `Foxes leap` can be normalized to a search for `fox jump`.
+
+[discrete]
+[[analysis-customization]]
+=== Customize text analysis
+
+Text analysis is performed by an <<analyzer-anatomy,_analyzer_>>, a set of rules
+that govern the entire process.
+
+{es} includes a default analyzer, called the
+<<analysis-standard-analyzer,standard analyzer>>, which works well for most use
+cases right out of the box.
+
+If you want to tailor your search experience, you can choose a different
+<<analysis-analyzers,built-in analyzer>> or even
+<<analysis-custom-analyzer,configure a custom one>>. A custom analyzer gives you
+control over each step of the analysis process, including:
+
+* Changes to the text _before_ tokenization
+
+* How text is converted to tokens
+
+* Normalization changes made to tokens before indexing or search