Azure · kristapratico · May 26, 2022 · May 25, 2022 · May 25, 2022 · May 25, 2022
@@ -3,7 +3,7 @@
 ## 1.1.0b1 (Unreleased)
 
 ### Features Added
-* Conversation issue summarization task (Long-running operation)
+* Conversation summarization task (Long-running operation)
 * Conversation PII extraction task (Long-running operation)
 
 ### Breaking Changes

@@ -4,7 +4,7 @@
 Conversational Language Understanding - aka **CLU** for short - is a cloud-based conversational AI service which provides many language understanding capabilities like:
 - Conversation App: It's used in extracting intents and entities in conversations
 - Workflow app: Acts like an orchestrator to select the best candidate to analyze conversations to get best response from apps like Qna, Luis, and Conversation App
-- Conversational Issue Summarization: Used to summarize conversations in the form of issues, and final resolutions
+- Conversational Summarization: Used to summarize conversations in the form of issues, and final resolutions
 - Conversational PII: Used to extract and redact personally-identifiable info (PII) 
 
 [Source code][conversationallanguage_client_src] | [Package (PyPI)][conversationallanguage_pypi_package] | [API reference documentation][api_reference_documentation] | [Product documentation][conversationallanguage_docs] | [Samples][conversationallanguage_samples]
@@ -200,7 +200,7 @@ if top_intent_object["targetProjectKind"] == "Luis":
 
 ```
 
-### Conversational Issue Summarization
+### Conversational Summarization
 
 You can use this sample if you need to summarize a conversation in the form of an issue, and final resolution. For example, a dialog from tech support:
 
@@ -267,13 +267,13 @@ with client:
     task_result = result["tasks"]["items"][0]
     print("... view task status ...")
     print("status: {}".format(task_result["status"]))
-    issue_resolution_result = task_result["results"]
-    if issue_resolution_result["errors"]:
+    resolution_result = task_result["results"]
+    if resolution_result["errors"]:
         print("... errors occured ...")
-        for error in issue_resolution_result["errors"]:
+        for error in resolution_result["errors"]:
             print(error)
     else:
-        conversation_result = issue_resolution_result["conversations"][0]
+        conversation_result = resolution_result["conversations"][0]
         if conversation_result["warnings"]:
             print("... view warnings ...")
             for warning in conversation_result["warnings"]:

@@ -5,7 +5,7 @@ languages:
 products:
   - azure
   - azure-cognitive-services
-  - azure-ai-language-understanding
+  - azure-language-service"
 urlFragment: conversationslanguageunderstanding-samples
 ---
 
@@ -25,7 +25,7 @@ These sample programs show common scenarios for the Conversational Language Unde
 | [sample_analyze_orchestration_app_conv_response.py][sample_analyze_orchestration_app_conv_response] and [sample_analyze_orchestration_app_conv_response_async.py][sample_analyze_orchestration_app_conv_response_async]| Analyze user utterance using an orchestration project, which selects the best candidate from one of your different apps to analyze user query (ex: Qna, Conversation, and Luis). In this case, it uses a conversation project. |
 | [sample_analyze_orchestration_app_luis_response.py][sample_analyze_orchestration_app_luis_response] and [sample_analyze_orchestration_app_luis_response_async.py][sample_analyze_orchestration_app_luis_response_async]| Analyze user utterance using an orchestration project, which selects the best candidate from one of your different apps to analyze user query (ex: Qna, Conversation, and Luis). In this case, it uses a Luis project. |
 | [sample_analyze_orchestration_app_qna_response.py][sample_analyze_orchestration_app_qna_response] and [sample_analyze_orchestration_app_qna_response_async.py][sample_analyze_orchestration_app_qna_response_async]| Analyze user utterance using an orchestration project, which selects the best candidate from one of your different apps to analyze user query (ex: Qna, Conversation, and Luis). In this case, it uses a Qna project. |
-| [sample_conv_issue_summarization.py][sample_conv_issue_summarization] and [sample_conv_issue_summarization_async.py][sample_conv_issue_summarization_async]| Summarize conversation in the form of issues and resolutions (ex: tech support conversation) |
+| [sample_conv_summarization.py][sample_conv_summarization] and [sample_conv_summarization_async.py][sample_conv_summarization_async]| Summarize conversation in the form of issues and resolutions (ex: tech support conversation) |
 | [sample_conv_pii_transcript_input.py][sample_conv_pii_transcript_input] and [sample_conv_pii_transcript_input_async.py][sample_conv_pii_transcript_input_async]| Extract and redact personally-identifiable info from/in conversations |
 
 ## Prerequisites
@@ -79,8 +79,8 @@ what you can do with the Azure Conversational Language Understanding client libr
 [sample_analyze_orchestration_app_qna_response]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/cognitivelanguage/azure-ai-language-conversations/samples/sample_analyze_orchestration_app_qna_response.py
 [sample_analyze_orchestration_app_qna_response_async]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/cognitivelanguage/azure-ai-language-conversations/samples/async/sample_analyze_orchestration_app_qna_response_async.py
 
-[sample_conv_issue_summarization]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/cognitivelanguage/azure-ai-language-conversations/samples/sample_conv_issue_summarization.py
-[sample_conv_issue_summarization_async]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/cognitivelanguage/azure-ai-language-conversations/samples/async/sample_conv_issue_summarization_async.py
+[sample_conv_summarization]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/cognitivelanguage/azure-ai-language-conversations/samples/sample_conv_summarization.py
+[sample_conv_summarization_async]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/cognitivelanguage/azure-ai-language-conversations/samples/async/sample_conv_summarization_async.py
 
 [sample_conv_pii_transcript_input]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/cognitivelanguage/azure-ai-language-conversations/samples/sample_conv_pii_transcript_input.py
 [sample_conv_pii_transcript_input_async]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/cognitivelanguage/azure-ai-language-conversations/samples/async/sample_conv_pii_transcript_input_async.py

@@ -5,15 +5,15 @@
 # ------------------------------------
 
 """
-FILE: sample_conv_issue_summarization_async.py
+FILE: sample_conv_summarization_async.py
 
 DESCRIPTION:
     This sample demonstrates how to analyze a conversation for issue resolution.
 
     For more info about how to setup a CLU conversation project, see the README.
 
 USAGE:
-    python sample_conv_issue_summarization_async.py
+    python sample_conv_summarization_async.py
 
     Set the environment variables with your own values before running the sample:
     1) AZURE_CONVERSATIONS_ENDPOINT                       - endpoint for your CLU resource.
@@ -22,7 +22,7 @@
 
 import asyncio
 
-async def sample_conv_issue_summarization_async():
+async def sample_conv_summarization_async():
     # [START analyze_conversation_app]
     # import libraries
     import os
@@ -86,13 +86,13 @@ async def sample_conv_issue_summarization_async():
         task_result = result["tasks"]["items"][0]
         print("... view task status ...")
         print("status: {}".format(task_result["status"]))
-        issue_resolution_result = task_result["results"]
-        if issue_resolution_result["errors"]:
+        resolution_result = task_result["results"]
+        if resolution_result["errors"]:
             print("... errors occured ...")
-            for error in issue_resolution_result["errors"]:
+            for error in resolution_result["errors"]:
                 print(error)
         else:
-            conversation_result = issue_resolution_result["conversations"][0]
+            conversation_result = resolution_result["conversations"][0]
             if conversation_result["warnings"]:
                 print("... view warnings ...")
                 for warning in conversation_result["warnings"]:
@@ -107,7 +107,7 @@ async def sample_conv_issue_summarization_async():
 
 
 async def main():
-    await sample_conv_issue_summarization_async()
+    await sample_conv_summarization_async()
 
 if __name__ == '__main__':
     loop = asyncio.get_event_loop()

@@ -5,22 +5,22 @@
 # ------------------------------------
 
 """
-FILE: sample_conv_issue_summarization.py
+FILE: sample_conv_summarization.py
 
 DESCRIPTION:
     This sample demonstrates how to analyze a conversation for issue resolution.
 
     For more info about how to setup a CLU conversation project, see the README.
 
 USAGE:
-    python sample_conv_issue_summarization.py
+    python sample_conv_summarization.py
 
     Set the environment variables with your own values before running the sample:
     1) AZURE_CONVERSATIONS_ENDPOINT                       - endpoint for your CLU resource.
     2) AZURE_CONVERSATIONS_KEY                            - API key for your CLU resource.
 """
 
-def sample_conv_issue_summarization():
+def sample_conv_summarization():
     # [START analyze_conversation_app]
     # import libraries
     import os
@@ -84,13 +84,13 @@ def sample_conv_issue_summarization():
         task_result = result["tasks"]["items"][0]
         print("... view task status ...")
         print("status: {}".format(task_result["status"]))
-        issue_resolution_result = task_result["results"]
-        if issue_resolution_result["errors"]:
+        resolution_result = task_result["results"]
+        if resolution_result["errors"]:
             print("... errors occured ...")
-            for error in issue_resolution_result["errors"]:
+            for error in resolution_result["errors"]:
                 print(error)
         else:
-            conversation_result = issue_resolution_result["conversations"][0]
+            conversation_result = resolution_result["conversations"][0]
             if conversation_result["warnings"]:
                 print("... view warnings ...")
                 for warning in conversation_result["warnings"]:
@@ -105,4 +105,4 @@ def sample_conv_issue_summarization():
 
 
 if __name__ == '__main__':
-    sample_conv_issue_summarization()
+    sample_conv_summarization()
@@ -20,7 +20,7 @@ autorest
 ### Settings
 
 ```yaml
-input-file: https://raw.githubusercontent.com/Azure/azure-rest-api-specs/d1716d13b0814a9d0785eda9a74529a315212f53/specification/cognitiveservices/data-plane/Language/preview/2022-05-15-preview/analyzeconversations.json
+input-file: https://raw.githubusercontent.com/Azure/azure-rest-api-specs/e7f37e4e43b1d12fd1988fda3ed39624c4b23303/specification/cognitiveservices/data-plane/Language/preview/2022-05-15-preview/analyzeconversations.json
 output-folder: ../azure/ai/language/conversations
 namespace: azure.ai.language.conversations
 package-name: azure-ai-language-conversations

@@ -14,10 +14,10 @@
 from azure.ai.language.conversations.aio import ConversationAnalysisClient
 
 
-class ConversationalIssueSummarizationAsyncTests(AsyncConversationTest):
+class ConversationalSummarizationAsyncTests(AsyncConversationTest):
 
     @GlobalConversationAccountPreparer()
-    async def test_conversational_issue_summarization(self, endpoint, key):
+    async def test_conversational_summarization(self, endpoint, key):
         # analyze query
         client = ConversationAnalysisClient(endpoint, AzureKeyCredential(key))
         async with client:

@@ -14,10 +14,10 @@
 )
 from azure.ai.language.conversations import ConversationAnalysisClient
 
-class ConversationalIssueSummarizationTests(ConversationTest):
+class ConversationalSummarizationTests(ConversationTest):
 
     @GlobalConversationAccountPreparer()
-    def test_conversational_issue_summarization(self, endpoint, key):
+    def test_conversational_summarization(self, endpoint, key):
         # analyze query
         client = ConversationAnalysisClient(endpoint, AzureKeyCredential(key))
         with client: