Correctness score fixes

Author: jboback

articles/cognitive-services/language-service/summarization/how-to/conversation-summarization.md

@@ -332,7 +332,7 @@ curl -i -X POST https://<your-language-resource-endpoint>/language/analyze-conve
 
 4. Paste the command from the text editor into the command prompt window, then run the command.
 
-5. Get the `operation-location` from the response header. The value will look similar ot the following URL:
+5. Get the `operation-location` from the response header. The value will look similar to the following URL:
 
 ```http
 https://<your-language-resource-endpoint>/language/analyze-conversations/jobs/12345678-1234-1234-1234-12345678?api-version=2022-10-01-preview

articles/cognitive-services/language-service/summarization/includes/quickstarts/rest-api.md

@@ -101,7 +101,7 @@ curl -i -X POST https://<your-language-resource-endpoint>/language/analyze-text/
 ```
 2. Make the following changes in the command where needed:
 - Replace the value `your-language-resource-key` with your key.
-- Replace the first part of te request URL `your-language-resource-endpoint` with your endpoint URL.
+- Replace the first part of the request URL `your-language-resource-endpoint` with your endpoint URL.
 
 3. Open a command prompt window (for example: BASH).
 
@@ -291,7 +291,7 @@ curl -i -X POST https://<your-language-resource-endpoint>/language/analyze-conve
 ```
 2. Make the following changes in the command where needed:
 - Replace the value `your-language-resource-key` with your key.
-- Replace the first part of te request URL `your-language-resource-endpoint` with your endpoint URL.
+- Replace the first part of the request URL `your-language-resource-endpoint` with your endpoint URL.
 
 3. Open a command prompt window (for example: BASH).
 

articles/cognitive-services/language-service/summarization/overview.md

@@ -28,7 +28,7 @@ This documentation contains the following article types:
 
 Document summarization uses natural language processing techniques to generate a summary for documents. There are two general approaches to automatic summarization, both of which are supported by the API: extractive and abstractive. 
 
-Extractive summarization extracts sentences that collectively represent the most important or relevant information within the original content. Abstractive summarization generates a summary with concise, coherent sentences or words which is not simply extract sentences from the original document. These features are designed to shorten content that could be considered too long to read.
+Extractive summarization extracts sentences that collectively represent the most important or relevant information within the original content. Abstractive summarization generates a summary with concise, coherent sentences or words which are not simply extract sentences from the original document. These features are designed to shorten content that could be considered too long to read.
 
 ## Key features
 
@@ -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/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](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: