Merge pull request #214261 from jboback/Summarization-Oct-Release

tamarakhader · web-flow · commit f9e0548ce7d9 · 2022-10-12T12:49:36.000-04:00
Link updates and doc ex sum change
diff --git a/articles/cognitive-services/language-service/summarization/how-to/document-summarization.md b/articles/cognitive-services/language-service/summarization/how-to/document-summarization.md
@@ -51,16 +51,6 @@ You submit documents to the API as strings of text. Analysis is performed upon r
 
 When using this feature, the API results are available for 24 hours from the time the request was ingested, and is indicated in the response. After this time period, the results are purged and are no longer available for retrieval.
 
-You can use the `sentenceCount` parameter to specify how many sentences will be returned, with `3` being the default. The range is from 1 to 20.
-
-You can also use the `sortby` parameter to specify in what order the extracted sentences will be returned - either `Offset` or `Rank`, with `Offset` being the default. 
-
-
-|parameter value  |Description  |
-|---------|---------|
-|Rank    | Order sentences according to their relevance to the input document, as decided by the service.        |
-|Offset    | Keeps the original order in which the sentences appear in the input document.        |
-
 ### Getting document summarization results
 
 When you get results from language detection, you can stream the results to an application or save the output to a file on the local system.
@@ -85,9 +75,18 @@ Using the above example, the API might return the following summarized sentences
 
 You can use document extractive summarization to get summaries of articles, papers, or documents. To see an example, see the [quickstart article](../quickstart.md).
 
+You can use the `sentenceCount` parameter to specify how many sentences will be returned, with `3` being the default. The range is from 1 to 20.
+
+You can also use the `sortby` parameter to specify in what order the extracted sentences will be returned - either `Offset` or `Rank`, with `Offset` being the default. 
+
+|parameter value  |Description  |
+|---------|---------|
+|Rank    | Order sentences according to their relevance to the input document, as decided by the service.        |
+|Offset    | Keeps the original order in which the sentences appear in the input document.        |
+
 ### Try document abstractive summarization
 
-[Reference documentation](https://go.microsoft.com/fwlink/?linkid=2195178)
+[Reference documentation](https://go.microsoft.com/fwlink/?linkid=2211684)
 
 The following example will get you started with document abstractive summarization:
 
diff --git a/articles/cognitive-services/language-service/summarization/includes/quickstarts/rest-api.md b/articles/cognitive-services/language-service/summarization/includes/quickstarts/rest-api.md
@@ -12,7 +12,7 @@ ms.custom: ignite-fall-2021, event-tier1-build-2022, ignite-2022
 
 # [Document summarization](#tab/document-summarization)
 
-[Reference documentation](https://go.microsoft.com/fwlink/?linkid=2195178)
+[Reference documentation](https://go.microsoft.com/fwlink/?linkid=2211684)
 
 # [Conversation summarization](#tab/conversation-summarization)
 
@@ -51,7 +51,7 @@ Choose the type of summarization you would like to perform, and select one of th
 
 # [Document summarization](#tab/document-summarization)
 
-[Reference documentation](https://go.microsoft.com/fwlink/?linkid=2195178)
+[Reference documentation](https://go.microsoft.com/fwlink/?linkid=2211684)
 
 |parameter  |Description  |
 |---------|---------|
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."*
 
-The 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.
+The 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](../concepts/multilingual-emoji-support.md) for more information.
 
 Using the above example, the API might return the following summarized sentences:
 
@@ -155,7 +155,7 @@ As you use document summarization in your applications, see the following refere
 
 |Development option / language  |Reference documentation |Samples  |
 |---------|---------|---------|
-|REST API     | [REST API documentation](https://go.microsoft.com/fwlink/?linkid=2195178)        |         |
+|REST API     | [REST API documentation](https://go.microsoft.com/fwlink/?linkid=2211684)        |         |
 |C#     | [C# documentation](/dotnet/api/azure.ai.textanalytics?view=azure-dotnet-preview&preserve-view=true)        | [C# samples](https://github.com/Azure/azure-sdk-for-net/tree/master/sdk/textanalytics/Azure.AI.TextAnalytics/samples)        |
 | Java     | [Java documentation](/java/api/overview/azure/ai-textanalytics-readme?view=azure-java-preview&preserve-view=true)        | [Java Samples](https://github.com/Azure/azure-sdk-for-java/tree/main/sdk/textanalytics/azure-ai-textanalytics/src/samples) |
 |JavaScript     | [JavaScript documentation](/javascript/api/overview/azure/ai-text-analytics-readme?view=azure-node-preview&preserve-view=true)        | [JavaScript samples](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/textanalytics/ai-text-analytics/samples/v5) |
