Final bug bash fixes

jboback · jboback · commit d5aadeb88412 · 2022-09-29T16:19:04.000-07:00
diff --git a/articles/cognitive-services/language-service/summarization/how-to/conversation-summarization.md b/articles/cognitive-services/language-service/summarization/how-to/conversation-summarization.md
@@ -86,7 +86,7 @@ Conversation issue and resolution summarization also enables you to get summarie
 
 ### Get chapter titles
 
-Conversation summarization lets you get chapter titles from input conversations. A guided example scenario is provided below:
+Conversation chapter title summarization lets you get chapter titles from input conversations. A guided example scenario is provided below:
 
 1. Copy the command below into a text editor. The BASH example uses the `\` line continuation character. If your console or terminal uses a different line continuation character, use that character.
 
@@ -188,47 +188,56 @@ curl -X GET https://<your-language-resource-endpoint>/language/analyze-conversat
 -H "Ocp-Apim-Subscription-Key: <your-language-resource-key>"
 ``` 
 
-Example JSON response:
+Example chapter title summarization JSON response:
 
 ```json
 {
-    "jobId": "13efaec1-896e-4da9-8b61-19db8408f26a",
-    "lastUpdatedDateTime": "2022-09-14T16:39:10Z",
-    "createdDateTime": "2022-09-14T16:39:08Z",
-    "expirationDateTime": "2022-09-15T16:39:08Z",
-    "status": "succeeded",
-    "errors": [],
-    "displayName": "Conversation Task Example",
-    "tasks": {
-        "completed": 1,
-        "failed": 0,
-        "inProgress": 0,
-        "total": 1,
-        "items": [
+  "jobId": "d874a98c-bf31-4ac5-8b94-5c236f786754",
+  "lastUpdatedDateTime": "2022-09-29T17:36:42Z",
+  "createdDateTime": "2022-09-29T17:36:39Z",
+  "expirationDateTime": "2022-09-30T17:36:39Z",
+  "status": "succeeded",
+  "errors": [],
+  "displayName": "Conversation Task Example",
+  "tasks": {
+    "completed": 1,
+    "failed": 0,
+    "inProgress": 0,
+    "total": 1,
+    "items": [
+      {
+        "kind": "conversationalSummarizationResults",
+        "taskName": "Conversation Task 1",
+        "lastUpdateDateTime": "2022-09-29T17:36:42.895694Z",
+        "status": "succeeded",
+        "results": {
+          "conversations": [
             {
-                "kind": "conversationalSummarizationResults",
-                "taskName": "Conversation Task 1",
-                "lastUpdateDateTime": "2022-09-14T16:39:10.7359603Z",
-                "status": "succeeded",
-                "results": {
-                    "conversations": [
-                        {
-                            "summaries": [
-                                {
-                                    "aspect": "chapterTitle",
-                                    "text": "Smart Brew 300 Espresso Machine WiFi Connection"
-                                }
-                            ],
-                            "id": "conversation1",
-                            "warnings": []
-                        }
-                    ],
-                    "errors": [],
-                    "modelVersion": "latest"
+              "summaries": [
+                {
+                  "aspect": "chapterTitle",
+                  "text": "Smart Brew 300 Espresso Machine WiFi Connection",
+                  "contexts": [
+                    { "conversationItemId": "1", "offset": 0, "length": 53 },
+                    { "conversationItemId": "2", "offset": 0, "length": 94 },
+                    { "conversationItemId": "3", "offset": 0, "length": 266 },
+                    { "conversationItemId": "4", "offset": 0, "length": 85 },
+                    { "conversationItemId": "5", "offset": 0, "length": 119 },
+                    { "conversationItemId": "6", "offset": 0, "length": 21 },
+                    { "conversationItemId": "7", "offset": 0, "length": 109 }
+                  ]
                 }
+              ],
+              "id": "conversation1",
+              "warnings": []
             }
-        ]
-    }
+          ],
+          "errors": [],
+          "modelVersion": "latest"
+        }
+      }
+    ]
+  }
 }
 ```
 For long conversation, the model might segment it into multiple cohesive parts, and summarize each segment. There is also a lengthy `contexts` field for each summary, which tells from which range of the input conversation we generated the summary.
@@ -337,47 +346,56 @@ curl -X GET https://<your-language-resource-endpoint>/language/analyze-conversat
 -H "Ocp-Apim-Subscription-Key: <your-language-resource-key>"
 ```
 
-Example JSON response:
+Example narrative summarization JSON response:
 
 ```json
 {
-    "jobId": "19561f16-abbc-430a-a50f-e2cdd7f3d998",
-    "lastUpdatedDateTime": "2022-09-14T16:42:35Z",
-    "createdDateTime": "2022-09-14T16:42:31Z",
-    "expirationDateTime": "2022-09-15T16:42:31Z",
-    "status": "succeeded",
-    "errors": [],
-    "displayName": "Conversation Task Example",
-    "tasks": {
-        "completed": 1,
-        "failed": 0,
-        "inProgress": 0,
-        "total": 1,
-        "items": [
+  "jobId": "d874a98c-bf31-4ac5-8b94-5c236f786754",
+  "lastUpdatedDateTime": "2022-09-29T17:36:42Z",
+  "createdDateTime": "2022-09-29T17:36:39Z",
+  "expirationDateTime": "2022-09-30T17:36:39Z",
+  "status": "succeeded",
+  "errors": [],
+  "displayName": "Conversation Task Example",
+  "tasks": {
+    "completed": 1,
+    "failed": 0,
+    "inProgress": 0,
+    "total": 1,
+    "items": [
+      {
+        "kind": "conversationalSummarizationResults",
+        "taskName": "Conversation Task 1",
+        "lastUpdateDateTime": "2022-09-29T17:36:42.895694Z",
+        "status": "succeeded",
+        "results": {
+          "conversations": [
             {
-                "kind": "conversationalSummarizationResults",
-                "taskName": "Conversation Task 1",
-                "lastUpdateDateTime": "2022-09-14T16:42:35.5714752Z",
-                "status": "succeeded",
-                "results": {
-                    "conversations": [
-                        {
-                            "summaries": [
-                                {
-                                    "aspect": "narrative",
-                                    "text": "Agent_1 helps customer to set up wifi connection for Smart Brew 300 espresso machine."
-                                }
-                            ],
-                            "id": "conversation1",
-                            "warnings": []
-                        }
-                    ],
-                    "errors": [],
-                    "modelVersion": "latest"
+              "summaries": [
+                {
+                  "aspect": "narrative",
+                  "text": "Agent_1 helps customer to set up wifi connection for Smart Brew 300 espresso machine.",
+                  "contexts": [
+                    { "conversationItemId": "1", "offset": 0, "length": 53 },
+                    { "conversationItemId": "2", "offset": 0, "length": 94 },
+                    { "conversationItemId": "3", "offset": 0, "length": 266 },
+                    { "conversationItemId": "4", "offset": 0, "length": 85 },
+                    { "conversationItemId": "5", "offset": 0, "length": 119 },
+                    { "conversationItemId": "6", "offset": 0, "length": 21 },
+                    { "conversationItemId": "7", "offset": 0, "length": 109 }
+                  ]
                 }
+              ],
+              "id": "conversation1",
+              "warnings": []
             }
-        ]
-    }
+          ],
+          "errors": [],
+          "modelVersion": "latest"
+        }
+      }
+    ]
+  }
 }
 ```
 
diff --git a/articles/cognitive-services/language-service/summarization/overview.md b/articles/cognitive-services/language-service/summarization/overview.md
@@ -47,7 +47,7 @@ As an example, consider the following paragraph of text:
 
 *"At Microsoft, we have been on a quest to advance AI beyond existing techniques, by taking a more holistic, human-centric approach to learning and understanding. As Chief Technology Officer of Azure AI Cognitive Services, I have been working with a team of amazing scientists and engineers to turn this quest into a reality. In my role, I enjoy a unique perspective in viewing the relationship among three attributes of human cognition: monolingual text (X), audio or visual sensory signals, (Y) and multilingual (Z). At the intersection of all three, there’s magic—what we call XYZ-code as illustrated in Figure 1—a joint representation to create more powerful AI that can speak, hear, see, and understand humans better. We believe XYZ-code will enable us to fulfill our long-term vision: cross-domain transfer learning, spanning modalities and languages. The goal is to have pre-trained models that can jointly learn representations to support a broad range of downstream AI tasks, much in the way humans do today. Over the past five years, we have achieved human performance on benchmarks in conversational speech recognition, machine translation, conversational question answering, machine reading comprehension, and image captioning. These five breakthroughs provided us with strong signals toward our more ambitious aspiration to produce a leap in AI capabilities, achieving multi-sensory and multilingual learning that is closer in line with how humans learn and understand. I believe the joint XYZ-code is a foundational component of this aspiration, if grounded with external knowledge sources in the downstream AI tasks."*
 
-he document summarization API request is processed upon receipt of the request by creating a job for the API backend. If the job succeeded, the output of the API will be returned. The output will be available for retrieval for 24 hours. After this time, the output is purged. Due to multilingual and emoji support, the response may contain text offsets. See [how to process offsets](https://learn.microsoft.com/en-us/azure/cognitive-services/language-service/concepts/multilingual-emoji-support) for more information.
+he document summarization API request is processed upon receipt of the request by creating a job for the API backend. If the job succeeded, the output of the API will be returned. The output will be available for retrieval for 24 hours. After this time, the output is purged. Due to multilingual and emoji support, the response may contain text offsets. See [how to process offsets](https://learn.microsoft.com/azure/cognitive-services/language-service/concepts/multilingual-emoji-support) for more information.
 
 Using the above example, the API might return the following summarized sentences:
 
@@ -131,7 +131,7 @@ To use this feature, you submit raw text for analysis and handle the API output
 
 |Development option  |Description  | Links |
 |---------|---------|---------|
-| REST API     | Integrate conversation summarization into your applications using the REST API. | [Quickstart: Use conversation summarization](quickstart?tabs=conversation-summarization&pivots=rest-api.md) |
+| REST API     | Integrate conversation summarization into your applications using the REST API. | [Quickstart: Use conversation summarization](quickstart.md?tabs=conversation-summarization&pivots=rest-api) |
 
 ---
 
