Updated Release Note (#415)

JohnSnowLabs · Jun 22, 2023 · a704483 · a704483
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diff --git a/docs/en/spark_nlp_healthcare_versions/licensed_release_notes.md b/docs/en/spark_nlp_healthcare_versions/licensed_release_notes.md
@@ -19,6 +19,7 @@ sidebar:
 
 We are delighted to announce a suite of remarkable enhancements and updates in our latest release of Spark NLP for Healthcare. This release comes with 40+ new clinical pretrained models and pipelines, and is a testament to our commitment to continuously innovate and improve, furnishing you with a more sophisticated and powerful toolkit for healthcare natural language processing.
 
++ Enhanced PySpark v3.4.X support for advanced Natural Language Processing
 + New module focused on extracting the most relevant information with Extractive Summarization
 + Customized prompts in `TextGenerator` Annotator
 + Arabic language obfuscation support in Deidentification
@@ -40,6 +41,11 @@ We believe that these enhancements will elevate your experience with Spark NLP f
 </div><div class="h3-box" markdown="1">
 
 
+#### Enhanced PySpark v3.4.X Support for Advanced Natural Language Processing
+
+SparkNLP, now offers enhanced support for PySpark v3.4, enabling data scientists and NLP practitioners to leverage the latest features and capabilities of Apache Spark while working with text data.
+
+
 #### New Module Focused On Extracting The Most Relevant Information With Extractive Summarization
 
 Extractive summarization focuses on extracting the most relevant information rather than generating new content. The process typically includes preprocessing the text, identifying important sentences using various criteria, ranking them based on their importance, and selecting the top-ranked sentences for the final summary. Extractive summarization is favored for its objectivity, preserving the factual accuracy of the original text.

diff --git a/docs/en/spark_nlp_healthcare_versions/release_notes_4_4_4.md b/docs/en/spark_nlp_healthcare_versions/release_notes_4_4_4.md
@@ -19,6 +19,7 @@ sidebar:
 
 We are delighted to announce a suite of remarkable enhancements and updates in our latest release of Spark NLP for Healthcare. This release comes with 40+ new clinical pretrained models and pipelines, and is a testament to our commitment to continuously innovate and improve, furnishing you with a more sophisticated and powerful toolkit for healthcare natural language processing.
 
++ Enhanced PySpark v3.4.X support for advanced Natural Language Processing
 + New module focused on extracting the most relevant information with Extractive Summarization
 + Customized prompts in `TextGenerator` Annotator
 + Arabic language obfuscation support in Deidentification
@@ -39,6 +40,10 @@ We believe that these enhancements will elevate your experience with Spark NLP f
 
 </div><div class="h3-box" markdown="1">
 
+#### Enhanced PySpark v3.4.X Support for Advanced Natural Language Processing
+
+SparkNLP, now offers enhanced support for PySpark v3.4, enabling data scientists and NLP practitioners to leverage the latest features and capabilities of Apache Spark while working with text data.
+
 
 #### New Module Focused On Extracting The Most Relevant Information With Extractive Summarization