Update multimodal-search-overview.md

Correcting Acrolynx pointers

Author: gmndrg

articles/search/multimodal-search-overview.md

@@ -27,7 +27,10 @@ Azure AI Search simplifies the construction of a multimodal pipeline through a g
 
 The functionality behind the **Import and vectorize data** wizard's multimodality option is powered by managed, configurable AI skills and the Azure Search knowledge store:
 
-+ [Document Intelligence layout skill](cognitive-search-skill-document-intelligence-layout.md) and [document extraction skill](cognitive-search-skill-document-extraction.md) obtain page text, inline images, and structural metadata. The Document Extraction skill doesn't support polygon extraction or page number extraction. Also, the range of supported file types may vary. To ensure optimal alignment with your specific use case, check each skill documentation for detailed information on compatibility and capabilities. The native document parsing mechanisms (document layout or document extraction skills) don't have support for table recognition or its structure preservation. If table extraction and its structure preservation support is required, it's recommended that a [Web API custom skill](cognitive-search-custom-skill-web-api.md) is built and call [Azure AI Content Understanding service](/azure/ai-services/content-understanding/tutorial/build-rag-solution) for content extraction (including tables). 
++ [Document Intelligence layout skill](cognitive-search-skill-document-intelligence-layout.md) and [document extraction skill](cognitive-search-skill-document-extraction.md) obtain page text, inline images, and structural metadata. The Document Extraction skill doesn't support polygon extraction or page number extraction. Also, the range of supported file types may vary. To ensure optimal alignment with your specific use case, check each skill documentation for detailed information on compatibility and capabilities.
+The native document extraction mechanisms (document layout or document extraction skills) don't support either table extraction or the preservation of their structure. To extract tables and retain their structure, you can:
+  1. Build a [custom Web API skill](cognitive-search-custom-skill-web-api.md).
+  1. Use this skill to call the [Azure AI Content Understanding service](/azure/ai-services/content-understanding/tutorial/build-rag-solution), which supports content extraction, including tables.
 + [Split skill](cognitive-search-skill-textsplit.md) chunks the extracted text for utilization in the remaining pipeline functionality (such as embedding skills). 
 + [Gen AI prompt skill](cognitive-search-skill-genai-prompt.md) verbalizes images, producing concise natural-language descriptions suitable for text search and embedding using a Large Language Model (LLM). 
 + Text/image (or multimodal) embedding skills create embeddings for text and images, enabling similarity and hybrid retrieval. You can call [Azure OpenAI](cognitive-search-skill-azure-openai-embedding.md), [AI Foundry](cognitive-search-aml-skill.md), or [AI Vision](cognitive-search-skill-vision-vectorize.md) embedding models natively.