[DOCS] Adding metadata to articles (#18331)

* adding-metadata

* Apply suggestions from code review

Co-authored-by: Tatiana Savina <tatiana.savina@intel.com>

* Apply suggestions from code review

Co-authored-by: Tatiana Savina <tatiana.savina@intel.com>

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Co-authored-by: Tatiana Savina <tatiana.savina@intel.com>

docs/optimization_guide/dldt_deployment_optimization_common.md

@@ -2,6 +2,12 @@
 
 @sphinxdirective
 
+.. meta::
+   :description: General optimizations include application-level optimization 
+                 methods that improve data pipelining, pre-processing 
+                 acceleration and both latency and throughput.
+
+
 This article covers application-level optimization techniques, such as asynchronous execution, to improve data pipelining, pre-processing acceleration and so on. 
 While the techniques (e.g. pre-processing) can be specific to end-user applications, the associated performance improvements are general and shall improve any target scenario -- both latency and throughput.
 

docs/optimization_guide/dldt_deployment_optimization_guide.md

@@ -16,6 +16,11 @@
    openvino_docs_deployment_optimization_guide_internals
    openvino_docs_memory_optimization_guide
 
+.. meta::
+   :description: Improving inference performance involves model and runtime 
+                 optimizations that can be done independently. Inference 
+                 speed depends on latency and throughput.
+
 
 Runtime optimization, or deployment optimization, focuses on tuning inference parameters and execution means (e.g., the optimum number of requests executed simultaneously). Unlike model-level optimizations, they are highly specific to the hardware and case they are used for, and often come at a cost.
 `ov::hint::inference_precision <groupov_runtime_cpp_prop_api.html#doxid-group-ov-runtime-cpp-prop-api-1gad605a888f3c9b7598ab55023fbf44240>`__ is a "typical runtime configuration" which trades accuracy for performance, allowing ``fp16/bf16`` execution for the layers that remain in ``fp32`` after quantization of the original ``fp32`` model.

docs/optimization_guide/dldt_deployment_optimization_internals.md

@@ -2,6 +2,12 @@
 
 @sphinxdirective
 
+.. meta::
+   :description: Automatic Batching moves asynchronicity from individual 
+                 requests to groups of requests, and the CPU streams are 
+                 inference threads grouped by CPU cores.
+
+
 Throughput on the CPU: Internals
 ################################
 

docs/optimization_guide/dldt_deployment_optimization_latency.md

@@ -8,6 +8,10 @@
 
    openvino_docs_OV_UG_Model_caching_overview
 
+.. meta::
+   :description: OpenVINO provides methods that help to preserve minimal 
+                 latency despite the number of inference requests and 
+                 improve throughput without degrading latency.
 
 
 A significant portion of deep learning use cases involve applications loading a single model and using a single input at a time, which is the of typical "consumer" scenario.

docs/optimization_guide/dldt_deployment_optimization_tput.md

@@ -2,6 +2,12 @@
 
 @sphinxdirective
 
+.. meta::
+   :description: Throughput-oriented approaches in OpenVINO involve 
+                 execution of a large number of inference requests 
+                 simultaneously which improves the device utilization.
+
+
 As described in the section on the :doc:`latency-specific considerations <openvino_docs_deployment_optimization_guide_latency>`, one of the possible use cases is *delivering every single request at the minimal delay*.
 Throughput, on the other hand, is about inference scenarios in which potentially **large number of inference requests are served simultaneously to improve the device utilization**.
 

docs/optimization_guide/dldt_deployment_optimization_tput_advanced.md

@@ -2,6 +2,12 @@
 
 @sphinxdirective
 
+.. meta::
+   :description: With OpenVINO streams a device may handle processing multiple 
+                 inference requests and the batching helps to saturate the 
+                 device and leads to higher throughput.
+
+
 OpenVINO Streams
 ####################