diff --git a/docs/_includes/docs-healthcare-pagination.html b/docs/_includes/docs-healthcare-pagination.html
index 5e9f3b425d..134988a2ec 100644
--- a/docs/_includes/docs-healthcare-pagination.html
+++ b/docs/_includes/docs-healthcare-pagination.html
@@ -10,6 +10,7 @@
     </li>
 </ul>
 <ul class="pagination owl-carousel pagination_big">
+    <li><a href="release_notes_4_4_2">4.4.2</a></li>
     <li><a href="release_notes_4_4_1">4.4.1</a></li>
     <li><a href="release_notes_4_4_0">4.4.0</a></li>
     <li><a href="release_notes_4_3_2">4.3.2</a></li>
diff --git a/docs/_posts/murat-gunay/2021-11-29-ner_deid_subentity_augmented_i2b2_en.md b/docs/_posts/murat-gunay/2021-11-29-ner_deid_subentity_augmented_i2b2_en.md
index daeb4d0e10..dfa9830774 100644
--- a/docs/_posts/murat-gunay/2021-11-29-ner_deid_subentity_augmented_i2b2_en.md
+++ b/docs/_posts/murat-gunay/2021-11-29-ner_deid_subentity_augmented_i2b2_en.md
@@ -62,7 +62,7 @@ deid_ner = MedicalNerModel.pretrained("ner_deid_subentity_augmented_i2b2", "en",
       .setInputCols(["sentence", "token", "embeddings"]) \
       .setOutputCol("ner")
 
-ner_converter = NerConverter()\
+ner_converter = NerConverterInternal()\
       .setInputCols(["sentence", "token", "ner"])\
       .setOutputCol("ner_chunk_subentity")
 
@@ -74,9 +74,9 @@ nlpPipeline = Pipeline(stages=[
       deid_ner, 
       ner_converter])
 
-model = nlpPipeline.fit(spark.createDataFrame([[""]]).toDF("text"))
+data = spark.createDataFrame([["""A. Record date : 2093-01-13, David Hale, M.D., Name : Hendrickson, Ora MR. # 7194334 Date : 01/13/93 PCP : Oliveira, 25 years old, Record date : 1-11-2000. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227. Patient's complaints first surfaced when he started working for Brothers Coal-Mine."""]]).toDF("text")
 
-results = model.transform(spark.createDataFrame(pd.DataFrame({"text": ["""A. Record date : 2093-01-13, David Hale, M.D., Name : Hendrickson, Ora MR. # 7194334 Date : 01/13/93 PCP : Oliveira, 25 years old, Record date : 1-11-2000. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227. Patient's complaints first surfaced when he started working for Brothers Coal-Mine."""]})))
+result = nlpPipeline.fit(data).transform(data)
 ```
 ```scala
 val document_assembler = new DocumentAssembler()
@@ -99,7 +99,7 @@ val deid_ner = MedicalNerModel.pretrained("ner_deid_subentity_augmented_i2b2", "
       .setInputCols(Array("sentence", "token", "embeddings")) 
       .setOutputCol("ner")
 
-val ner_converter = NerConverter()
+val ner_converter = NerConverterInternal()
       .setInputCols(Array("sentence", "token", "ner"))
       .setOutputCol("ner_chunk_subentity")
 
@@ -111,7 +111,9 @@ val nlpPipeline = new Pipeline().setStages(Array(
       deid_ner, 
       ner_converter))
 
-val result = nlpPipeline.fit(Seq("""A. Record date : 2093-01-13, David Hale, M.D., Name : Hendrickson, Ora MR. # 7194334 Date : 01/13/93 PCP : Oliveira, 25 years old, Record date : 1-11-2000. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227. Patient's complaints first surfaced when he started working for Brothers Coal-Mine.""").toDS.toDF("text")).transform(data)
+val data = Seq("""A. Record date : 2093-01-13, David Hale, M.D., Name : Hendrickson, Ora MR. # 7194334 Date : 01/13/93 PCP : Oliveira, 25 years old, Record date : 1-11-2000. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227. Patient's complaints first surfaced when he started working for Brothers Coal-Mine.""").toDS.toDF("text")
+
+val result = nlpPipeline.fit(data).transform(data)
 ```
 
 
@@ -166,27 +168,27 @@ In-house annotations based on `2014 i2b2 Deid dataset`.
 (on official test set from 2014 i2b2 Deid Data-set)
 
 ```bash
-label  precision    recall  f1-score   support
-AGE       0.96      0.96      0.96       764
-CITY       0.83      0.84      0.84       260
-COUNTRY       0.79      0.85      0.82       117
-DATE       0.97      0.97      0.97      4980
-DEVICE       0.88      0.88      0.88         8
-DOCTOR       0.94      0.88      0.91      1912
-HOSPITAL       0.91      0.83      0.87       875
-IDNUM       0.84      0.85      0.84       195
-LOCATION-OTHER       0.86      0.46      0.60        13
-MEDICALRECORD       0.98      0.95      0.96       422
-ORGANIZATION       0.83      0.59      0.69        82
-PATIENT       0.93      0.93      0.93       879
-PHONE       0.93      0.91      0.92       215
+label       precision    recall  f1-score   support
+AGE              0.96      0.96      0.96       764
+CITY             0.83      0.84      0.84       260
+COUNTRY          0.79      0.85      0.82       117
+DATE             0.97      0.97      0.97      4980
+DEVICE           0.88      0.88      0.88         8
+DOCTOR           0.94      0.88      0.91      1912
+HOSPITAL         0.91      0.83      0.87       875
+IDNUM            0.84      0.85      0.84       195
+LOCATION-OTHER   0.86      0.46      0.60        13
+MEDICALRECORD    0.98      0.95      0.96       422
+ORGANIZATION     0.83      0.59      0.69        82
+PATIENT          0.93      0.93      0.93       879
+PHONE            0.93      0.91      0.92       215
 PROFESSION       0.84      0.75      0.79       179
-STATE       0.95      0.86      0.90       190
-STREET       0.96      0.97      0.97       136
-USERNAME       1.00      0.96      0.98        92
-ZIP       0.98      0.99      0.98       140
-micro-avg       0.95      0.92      0.94     11459
-macro-avg       0.86      0.81      0.83     11459
-weighted-avg       0.95      0.92      0.93     11459
+STATE            0.95      0.86      0.90       190
+STREET           0.96      0.97      0.97       136
+USERNAME         1.00      0.96      0.98        92
+ZIP              0.98      0.99      0.98       140
+micro-avg        0.95      0.92      0.94     11459
+macro-avg        0.86      0.81      0.83     11459
+weighted-avg     0.95      0.92      0.93     11459
 ```
 `FAX` and `EMAIL` has been removed from official i2b2 test-set since there is not enough data to train in the official i2b2 train-set.
diff --git a/docs/en/licensed_annotator_entries/NerChunker.md b/docs/en/licensed_annotator_entries/NerChunker.md
index 135855f542..17158f42bb 100644
--- a/docs/en/licensed_annotator_entries/NerChunker.md
+++ b/docs/en/licensed_annotator_entries/NerChunker.md
@@ -332,7 +332,7 @@ val pipeline=new Pipeline().setStages(Array(
 {%- endcapture -%}
 
 {%- capture model_api_link -%}
-[NerChunker](https://nlp.johnsnowlabs.com/licensed/api/com/johnsnowlabs/nlp/annotators/ner/NerChunker)
+[NerChunker](https://nlp.johnsnowlabs.com/licensed/api/com/johnsnowlabs/nlp/annotators/ner/NerChunker.html)
 {%- endcapture -%}
 
 {%- capture model_python_api_link -%}
diff --git a/docs/en/licensed_annotator_entries/Summarizer.md b/docs/en/licensed_annotator_entries/Summarizer.md
index 99b7952271..72a12b3423 100644
--- a/docs/en/licensed_annotator_entries/Summarizer.md
+++ b/docs/en/licensed_annotator_entries/Summarizer.md
@@ -29,13 +29,17 @@ document_assembler = nlp.DocumentAssembler()\
     .setInputCol("text")\
     .setOutputCol("documents")
 
-
 med_summarizer  = medical.Summarizer\
     .pretrained("summarizer_generic_jsl", "en", "clinical/models")\
     .setInputCols("documents")\
     .setOutputCol("summary")\
-    .setMaxNewTokens(100)\
-    .setMaxTextLength(1024)\
+    .setMaxTextLength(512)\
+    .setMaxNewTokens(512)\
+    .setDoSample(True)\
+    .setRefineSummary(True)\
+    .setRefineSummaryTargetLength(100)\
+    .setRefineMaxAttempts(3)\
+    .setRefineChunkSize(512)\
 
 
 pipeline = nlp.Pipeline(stages=[document_assembler, med_summarizer])
@@ -70,7 +74,13 @@ val document_assembler = new nlp.DocumentAssembler()
 val med_summarizer  = medical.Summarizer.pretrained("summarizer_generic_jsl", "en", "clinical/models")
     .setInputCols("documents")
     .setOutputCol("summary")
-    .setMaxNewTokens(100)
+    .setMaxTextLength(512)
+    .setMaxNewTokens(512)
+    .setDoSample(true)
+    .setRefineSummary(true)
+    .setRefineSummaryTargetLength(100)
+    .setRefineMaxAttempts(3)
+    .setRefineChunkSize(512)
 
 
 val pipeline = new nlp.Pipeline().setStages(Array(document_assembler, med_summarizer))
@@ -104,8 +114,13 @@ med_summarizer  = legal.Summarizer\
     .pretrained("summarizer_generic_jsl", "en", "clinical/models")\
     .setInputCols("documents")\
     .setOutputCol("summary")\
-    .setMaxNewTokens(100)\
-   .setMaxTextLength(1024)\
+    .setMaxTextLength(512)\
+    .setMaxNewTokens(512)\
+    .setDoSample(True)\
+    .setRefineSummary(True)\
+    .setRefineSummaryTargetLength(100)\
+    .setRefineMaxAttempts(3)\
+    .setRefineChunkSize(512)\
 
 
 pipeline = nlp.Pipeline(stages=[document_assembler, med_summarizer])
@@ -141,7 +156,13 @@ val document_assembler = new nlp.DocumentAssembler()
 val med_summarizer  = legal.Summarizer.pretrained("summarizer_generic_jsl", "en", "clinical/models")
     .setInputCols("documents")
     .setOutputCol("summary")
-    .setMaxNewTokens(100)
+    .setMaxTextLength(512)
+    .setMaxNewTokens(512)
+    .setDoSample(true)
+    .setRefineSummary(true)
+    .setRefineSummaryTargetLength(100)
+    .setRefineMaxAttempts(3)
+    .setRefineChunkSize(512)
 
 
 val pipeline = new nlp.Pipeline().setStages(Array(document_assembler, med_summarizer))
@@ -178,7 +199,12 @@ med_summarizer  = finance.Summarizer\
     .setInputCols("documents")\
     .setOutputCol("summary")\
     .setMaxNewTokens(100)\
-   .setMaxTextLength(1024)\
+    .setMaxTextLength(1024)\
+    .setDoSample(True)\
+    .setRefineSummary(True)\
+    .setRefineSummaryTargetLength(100)\
+    .setRefineMaxAttempts(3)\
+    .setRefineChunkSize(512)\
 
 
 pipeline = nlp.Pipeline(stages=[document_assembler, med_summarizer])
@@ -213,7 +239,13 @@ val document_assembler = new nlp.DocumentAssembler()
 val med_summarizer  = finance.Summarizer.pretrained("summarizer_generic_jsl", "en", "clinical/models")
     .setInputCols("documents")
     .setOutputCol("summary")
-    .setMaxNewTokens(100)
+    .setMaxTextLength(512)
+    .setMaxNewTokens(512)
+    .setDoSample(True)
+    .setRefineSummary(True)
+    .setRefineSummaryTargetLength(100)
+    .setRefineMaxAttempts(3)
+    .setRefineChunkSize(512)
 
 
 val pipeline = new nlp.Pipeline().setStages(Array(document_assembler, med_summarizer))