Quickstart: using text, document and conversation summarization

  • Article
Choose one of the client library languages or REST API.

Important

Conversation summarization is only available using:

  • REST API
  • Python
  • C#

Reference documentation | More samples | Package (NuGet) | Library source code

Use this quickstart to create a text summarization application with the client library for .NET. In the following example, you'll create a C# application that can summarize documents or text-based customer service conversations.

Tip

You can use Language Studio to try text summarization without needing to write code.

Prerequisites

  • Azure subscription - Create one for trial
  • The Visual Studio IDE
  • Once you have your Azure subscription, create an AI services resource.
    • You'll need the key and endpoint from the resource you create to connect your application to the API. You paste your key and endpoint into the code later in the quickstart.
    • You can use the free pricing tier (Free F0) to try the service, and upgrade later to a paid tier for production.
  • To use the Analyze feature, you'll need a Language resource with the standard (S) pricing tier.

Setting up

Create environment variables

Your application must be authenticated to send API requests. For production, use a secure way of storing and accessing your credentials. In this example, you will write your credentials to environment variables on the local machine running the application.

To set the environment variable for your Language resource key, open a console window, and follow the instructions for your operating system and development environment.

  • To set the LANGUAGE_KEY environment variable, replace your-key with one of the keys for your resource.
  • To set the LANGUAGE_ENDPOINT environment variable, replace your-endpoint with the endpoint for your resource.

Important

We recommend Microsoft Entra ID authentication with managed identities for Azure resources to avoid storing credentials with your applications that run in the cloud.

Use API keys with caution. Don't include the API key directly in your code, and never post it publicly. If using API keys, store them securely in Azure Key Vault, rotate the keys regularly, and restrict access to Azure Key Vault using role based access control and network access restrictions.

For more information about AI services security, see Authenticate requests to Azure AI services.

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

After you add the environment variables, run source ~/.bashrc from your console window to make the changes effective.

Create a new .NET Core application

Using the Visual Studio IDE, create a new .NET Core console app. This creates a "Hello World" project with a single C# source file: program.cs.

Install the client library by right-clicking on the solution in the Solution Explorer and selecting Manage NuGet Packages. In the package manager that opens select Browse and search for Azure.AI.Language.Conversations. Make sure Include prerelease is checked. Select version 1.1.0, and then Install. You can also use the Package Manager Console.

Code example

Copy the following code into your program.cs file. Then run the code.

Important

Go to the Azure portal. If the Language resource you created in the Prerequisites section deployed successfully, click the Go to Resource button under Next Steps. You can find your key and endpoint by navigating to your resource's Keys and Endpoint page, under Resource Management.

Important

Remember to remove the key from your code when you're done, and never post it publicly. For production, use a secure way of storing and accessing your credentials like Azure Key Vault. See the Azure AI services security article for more information.

using System;
using Azure;
using Azure.Core;
using Azure.AI.Language.Conversations;
using System.Text.Json;
using System.Threading.Tasks;

namespace Example
{
    class Program
    {
        private static readonly AzureKeyCredential credentials = new AzureKeyCredential("replace-with-your-key-here");
        private static readonly Uri endpoint = new Uri("replace-with-your-endpoint-here");

        static async Task Main(string[] args)
        {
            ConversationAnalysisClient client = new ConversationAnalysisClient(endpoint, credentials);
            await Summarization(client);
        }

        static async Task Summarization(ConversationAnalysisClient client)
        {
            var data = new
            {
                analysisInput = new
                {
                    conversations = new[]
                    {
                        new
                        {
                            conversationItems = new[]
                            {
                                new
                                {
                                    text = "Hello, you’re chatting with Rene. How may I help you?",
                                    id = "1",
                                    role = "Agent",
                                    participantId = "Agent",
                                },
                                new
                                {
                                    text = "Hi, I tried to set up wifi connection for Smart Brew 300 coffee machine, but it didn’t work.",
                                    id = "2",
                                    role = "Customer",
                                    participantId = "Customer",
                                },
                                new
                                {
                                    text = "I’m sorry to hear that. Let’s see what we can do to fix this issue. Could you please try the following steps for me? First, could you push the wifi connection button, hold for 3 seconds, then let me know if the power light is slowly blinking on and off every second?",
                                    id = "3",
                                    role = "Agent",
                                    participantId = "Agent",
                                },
                                new
                                {
                                    text = "Yes, I pushed the wifi connection button, and now the power light is slowly blinking?",
                                    id = "4",
                                    role = "Customer",
                                    participantId = "Customer",
                                },
                                new
                                {
                                    text = "Great. Thank you! Now, please check in your Contoso Coffee app. Does it prompt to ask you to connect with the machine?",
                                    id = "5",
                                    role = "Agent",
                                    participantId = "Agent",
                                },
                                new
                                {
                                    text = "No. Nothing happened.",
                                    id = "6",
                                    role = "Customer",
                                    participantId = "Customer",
                                },
                                new
                                {
                                    text = "I’m very sorry to hear that. Let me see if there’s another way to fix the issue. Please hold on for a minute.",
                                    id = "7",
                                    role = "Agent",
                                    participantId = "Agent",
                                }
                            },
                            id = "1",
                            language = "en",
                            modality = "text",
                        },
                    }
                },
                tasks = new[]
                {
                    new
                    {
                        parameters = new
                        {
                            summaryAspects = new[]
                            {
                                "issue",
                                "resolution",
                            }
                        },
                        kind = "ConversationalSummarizationTask",
                        taskName = "1",
                    },
                },
            };

            Operation<BinaryData> analyzeConversationOperation = await client.AnalyzeConversationsAsync(WaitUntil.Started, RequestContent.Create(data));
            analyzeConversationOperation.WaitForCompletion();

            using JsonDocument result = JsonDocument.Parse(analyzeConversationOperation.Value.ToStream());
            JsonElement jobResults = result.RootElement;
            foreach (JsonElement task in jobResults.GetProperty("tasks").GetProperty("items").EnumerateArray())
            {
                JsonElement results = task.GetProperty("results");

                Console.WriteLine("Conversations:");
                foreach (JsonElement conversation in results.GetProperty("conversations").EnumerateArray())
                {
                    Console.WriteLine($"Conversation: #{conversation.GetProperty("id").GetString()}");
                    Console.WriteLine("Summaries:");
                    foreach (JsonElement summary in conversation.GetProperty("summaries").EnumerateArray())
                    {
                        Console.WriteLine($"Text: {summary.GetProperty("text").GetString()}");
                        Console.WriteLine($"Aspect: {summary.GetProperty("aspect").GetString()}");
                    }
                    Console.WriteLine();
                }
            }
        }
    }
}

Output

Conversations:
Conversation: #1
Summaries:
Text: Customer is unable to set up wifi connection for Smart Brew 300 coffee machine.
Aspect: issue
Text: The agent instructed the customer to press the wifi connection button, hold for 3 seconds, and check if the power light blinks. The agent also asked the customer to check if the Contoso Coffee app prompts to connect with the machine, but the issue persisted. The agent is looking for another way to fix the issue.
Aspect: resolution

Reference documentation | More samples | Package (Maven) | Library source code

Use this quickstart to create a text summarization application with the client library for Java. In the following example, you'll create a Java application that can summarize documents.

Tip

You can use Language Studio to try text summarization without needing to write code.

Prerequisites

  • Azure subscription - Create one for trial
  • Java Development Kit (JDK) with version 8 or above
  • Once you have your Azure subscription, create an AI services resource.
    • You'll need the key and endpoint from the resource you create to connect your application to the API. You paste your key and endpoint into the code below later in the quickstart.
    • You can use the free pricing tier (Free F0) to try the service, and upgrade later to a paid tier for production.
  • To use the Analyze feature, you'll need a Language resource with the standard (S) pricing tier.

Setting up

Add the client library

Create a Maven project in your preferred IDE or development environment. Then add the following dependency to your project's pom.xml file. You can find the implementation syntax for other build tools online.

<dependencies>
     <dependency>
        <groupId>com.azure</groupId>
        <artifactId>azure-ai-textanalytics</artifactId>
        <version>5.3.0</version>
    </dependency>
</dependencies>

Create environment variables

Your application must be authenticated to send API requests. For production, use a secure way of storing and accessing your credentials. In this example, you will write your credentials to environment variables on the local machine running the application.

To set the environment variable for your Language resource key, open a console window, and follow the instructions for your operating system and development environment.

  • To set the LANGUAGE_KEY environment variable, replace your-key with one of the keys for your resource.
  • To set the LANGUAGE_ENDPOINT environment variable, replace your-endpoint with the endpoint for your resource.

Important

We recommend Microsoft Entra ID authentication with managed identities for Azure resources to avoid storing credentials with your applications that run in the cloud.

Use API keys with caution. Don't include the API key directly in your code, and never post it publicly. If using API keys, store them securely in Azure Key Vault, rotate the keys regularly, and restrict access to Azure Key Vault using role based access control and network access restrictions.

For more information about AI services security, see Authenticate requests to Azure AI services.

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

After you add the environment variables, run source ~/.bashrc from your console window to make the changes effective.

Code example

Create a Java file named Example.java. Open the file and copy the below code. Then run the code.

Important

Go to the Azure portal. If the Language resource you created in the Prerequisites section deployed successfully, click the Go to Resource button under Next Steps. You can find your key and endpoint by navigating to your resource's Keys and Endpoint page, under Resource Management.

Important

Remember to remove the key from your code when you're done, and never post it publicly. For production, use a secure way of storing and accessing your credentials like Azure Key Vault. See the Azure AI services security article for more information.

import com.azure.core.credential.AzureKeyCredential;
import com.azure.ai.textanalytics.models.*;
import com.azure.ai.textanalytics.TextAnalyticsClientBuilder;
import com.azure.ai.textanalytics.TextAnalyticsClient;
import java.util.ArrayList;
import java.util.List;
import com.azure.core.util.polling.SyncPoller;
import com.azure.ai.textanalytics.util.*;

public class Example {

    // This example requires environment variables named "LANGUAGE_KEY" and "LANGUAGE_ENDPOINT"
    private static String languageKey = System.getenv("LANGUAGE_KEY");
    private static String languageEndpoint = System.getenv("LANGUAGE_ENDPOINT");

    public static void main(String[] args) {
        TextAnalyticsClient client = authenticateClient(languageKey, languageEndpoint);
        summarizationExample(client);
    }
    // Method to authenticate the client object with your key and endpoint
    static TextAnalyticsClient authenticateClient(String key, String endpoint) {
        return new TextAnalyticsClientBuilder()
                .credential(new AzureKeyCredential(key))
                .endpoint(endpoint)
                .buildClient();
    }
    // Example method for summarizing text
    static void summarizationExample(TextAnalyticsClient client) {
        List<String> documents = new ArrayList<>();
        documents.add(
                "The extractive summarization feature uses natural language processing techniques "
                + "to locate key sentences in an unstructured text document. "
                + "These sentences collectively convey the main idea of the document. This feature is provided as an API for developers. "
                + "They can use it to build intelligent solutions based on the relevant information extracted to support various use cases. "
                + "Extractive summarization supports several languages. "
                + "It is based on pretrained multilingual transformer models, part of our quest for holistic representations. "
                + "It draws its strength from transfer learning across monolingual and harness the shared nature of languages "
                + "to produce models of improved quality and efficiency.");
    
        SyncPoller<AnalyzeActionsOperationDetail, AnalyzeActionsResultPagedIterable> syncPoller =
                client.beginAnalyzeActions(documents,
                        new TextAnalyticsActions().setDisplayName("{tasks_display_name}")
                                .setExtractSummaryActions(
                                        new ExtractSummaryAction()),
                        "en",
                        new AnalyzeActionsOptions());
    
        syncPoller.waitForCompletion();
    
        syncPoller.getFinalResult().forEach(actionsResult -> {
            System.out.println("Extractive Summarization action results:");
            for (ExtractSummaryActionResult actionResult : actionsResult.getExtractSummaryResults()) {
                if (!actionResult.isError()) {
                    for (ExtractSummaryResult documentResult : actionResult.getDocumentsResults()) {
                        if (!documentResult.isError()) {
                            System.out.println("\tExtracted summary sentences:");
                            for (SummarySentence summarySentence : documentResult.getSentences()) {
                                System.out.printf(
                                        "\t\t Sentence text: %s, length: %d, offset: %d, rank score: %f.%n",
                                        summarySentence.getText(), summarySentence.getLength(),
                                        summarySentence.getOffset(), summarySentence.getRankScore());
                            }
                        } else {
                            System.out.printf("\tCannot extract summary sentences. Error: %s%n",
                                    documentResult.getError().getMessage());
                        }
                    }
                } else {
                    System.out.printf("\tCannot execute Extractive Summarization action. Error: %s%n",
                            actionResult.getError().getMessage());
                }
            }
        });
    }
}

Output

Extractive Summarization action results:
	Extracted summary sentences:
		 Sentence text: The extractive summarization feature uses natural language processing techniques to locate key sentences in an unstructured text document., length: 138, offset: 0, rank score: 1.000000.
		 Sentence text: This feature is provided as an API for developers., length: 50, offset: 206, rank score: 0.510000.
		 Sentence text: Extractive summarization supports several languages., length: 52, offset: 378, rank score: 0.410000.

Reference documentation | Additional samples | Package (npm) | Library source code

Use this quickstart to create a text summarization application with the client library for Node.js. In the following example, you create a JavaScript application that can summarize documents.

Tip

You can use Language Studio to try text summarization without needing to write code.

Prerequisites

  • Azure subscription - Create one for trial
  • Node.js v16 LTS
  • Once you have your Azure subscription, create an AI services resource.
    • You need the key and endpoint from the resource you create to connect your application to the API. You paste your key and endpoint into the code below later in the quickstart.
    • You can use the free pricing tier (Free F0) to try the service, and upgrade later to a paid tier for production.
  • To use the Analyze feature, you need a Language resource with the standard (S) pricing tier.

Setting up

Create environment variables

Your application must be authenticated to send API requests. For production, use a secure way of storing and accessing your credentials. In this example, you will write your credentials to environment variables on the local machine running the application.

To set the environment variable for your Language resource key, open a console window, and follow the instructions for your operating system and development environment.

  • To set the LANGUAGE_KEY environment variable, replace your-key with one of the keys for your resource.
  • To set the LANGUAGE_ENDPOINT environment variable, replace your-endpoint with the endpoint for your resource.

Important

We recommend Microsoft Entra ID authentication with managed identities for Azure resources to avoid storing credentials with your applications that run in the cloud.

Use API keys with caution. Don't include the API key directly in your code, and never post it publicly. If using API keys, store them securely in Azure Key Vault, rotate the keys regularly, and restrict access to Azure Key Vault using role based access control and network access restrictions.

For more information about AI services security, see Authenticate requests to Azure AI services.

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

After you add the environment variables, run source ~/.bashrc from your console window to make the changes effective.

Create a new Node.js application

In a console window (such as cmd, PowerShell, or Bash), create a new directory for your app, and navigate to it.

mkdir myapp 

cd myapp

Run the npm init command to create a node application with a package.json file.

npm init

Install the client library

Install the npm packages:

npm install --save @azure/ai-language-text@1.1.0

Code example

Open the file and copy the below code. Then run the code.

Important

Go to the Azure portal. If the Language resource you created in the Prerequisites section deployed successfully, click the Go to Resource button under Next Steps. You can find your key and endpoint by navigating to your resource's Keys and Endpoint page, under Resource Management.

Important

Remember to remove the key from your code when you're done, and never post it publicly. For production, use a secure way of storing and accessing your credentials like Azure Key Vault. See the Azure AI services security article for more information.

/**
 * This sample program extracts a summary of two sentences at max from an article.
 * For more information, see the feature documentation: {@link https://learn.microsoft.com/azure/ai-services/language-service/summarization/overview}
 *
 * @summary extracts a summary from an article
 */

const { AzureKeyCredential, TextAnalysisClient } = require("@azure/ai-language-text");

// Load the .env file if it exists
require("dotenv").config();

// This example requires environment variables named "LANGUAGE_KEY" and "LANGUAGE_ENDPOINT"
const endpoint = process.env.LANGUAGE_ENDPOINT;
const apiKey = process.env.LANGUAGE_KEY;

const documents = [
  `
           Windows 365 was in the works before COVID-19 sent companies around the world on a scramble to secure solutions to support employees suddenly forced to work from home, but “what really put the firecracker behind it was the pandemic, it accelerated everything,” McKelvey said. She explained that customers were asking, “’How do we create an experience for people that makes them still feel connected to the company without the physical presence of being there?”
           In this new world of Windows 365, remote workers flip the lid on their laptop, bootup the family workstation or clip a keyboard onto a tablet, launch a native app or modern web browser and login to their Windows 365 account. From there, their Cloud PC appears with their background, apps, settings and content just as they left it when they last were last there - in the office, at home or a coffee shop.
           “And then, when you’re done, you’re done. You won’t have any issues around security because you’re not saving anything on your device,” McKelvey said, noting that all the data is stored in the cloud.
           The ability to login to a Cloud PC from anywhere on any device is part of Microsoft’s larger strategy around tailoring products such as Microsoft Teams and Microsoft 365 for the post-pandemic hybrid workforce of the future, she added. It enables employees accustomed to working from home to continue working from home; it enables companies to hire interns from halfway around the world; it allows startups to scale without requiring IT expertise.
           “I think this will be interesting for those organizations who, for whatever reason, have shied away from virtualization. This is giving them an opportunity to try it in a way that their regular, everyday endpoint admin could manage,” McKelvey said.
           The simplicity of Windows 365 won over Dean Wells, the corporate chief information officer for the Government of Nunavut. His team previously attempted to deploy a traditional virtual desktop infrastructure and found it inefficient and unsustainable given the limitations of low-bandwidth satellite internet and the constant need for IT staff to manage the network and infrastructure.
           We didn’t run it for very long,” he said. “It didn’t turn out the way we had hoped. So, we actually had terminated the project and rolled back out to just regular PCs.”
           He re-evaluated this decision after the Government of Nunavut was hit by a ransomware attack in November 2019 that took down everything from the phone system to the government’s servers. Microsoft helped rebuild the system, moving the government to Teams, SharePoint, OneDrive and Microsoft 365. Manchester’s team recruited the Government of Nunavut to pilot Windows 365. Wells was intrigued, especially by the ability to manage the elastic workforce securely and seamlessly.
           “The impact that I believe we are finding, and the impact that we’re going to find going forward, is being able to access specialists from outside the territory and organizations outside the territory to come in and help us with our projects, being able to get people on staff with us to help us deliver the day-to-day expertise that we need to run the government,” he said.
           “Being able to improve healthcare, being able to improve education, economic development is going to improve the quality of life in the communities.”`,
];

async function main() {
  console.log("== Extractive Summarization Sample ==");

  const client = new TextAnalysisClient(endpoint, new AzureKeyCredential(apiKey));
  const actions = [
    {
      kind: "ExtractiveSummarization",
      maxSentenceCount: 2,
    },
  ];
  const poller = await client.beginAnalyzeBatch(actions, documents, "en");

  poller.onProgress(() => {
    console.log(
      `Last time the operation was updated was on: ${poller.getOperationState().modifiedOn}`
    );
  });
  console.log(`The operation was created on ${poller.getOperationState().createdOn}`);
  console.log(`The operation results will expire on ${poller.getOperationState().expiresOn}`);

  const results = await poller.pollUntilDone();

  for await (const actionResult of results) {
    if (actionResult.kind !== "ExtractiveSummarization") {
      throw new Error(`Expected extractive summarization results but got: ${actionResult.kind}`);
    }
    if (actionResult.error) {
      const { code, message } = actionResult.error;
      throw new Error(`Unexpected error (${code}): ${message}`);
    }
    for (const result of actionResult.results) {
      console.log(`- Document ${result.id}`);
      if (result.error) {
        const { code, message } = result.error;
        throw new Error(`Unexpected error (${code}): ${message}`);
      }
      console.log("Summary:");
      console.log(result.sentences.map((sentence) => sentence.text).join("\n"));
    }
  }
}

main().catch((err) => {
  console.error("The sample encountered an error:", err);
});

module.exports = { main };

Use this quickstart to create a text summarization application with the client library for Python. In the following example, you'll create a Python application that can summarize documents or text-based customer service conversations.

Tip

You can use Language Studio to try text summarization without needing to write code.

Prerequisites

  • Azure subscription - Create one for trial
  • Python 3.x
  • Once you have your Azure subscription, create an AI services resource.
    • You'll need the key and endpoint from the resource you create to connect your application to the API. You paste your key and endpoint into the code below later in the quickstart.
    • You can use the free pricing tier (Free F0) to try the service, and upgrade later to a paid tier for production.
  • To use the Analyze feature, you'll need a Language resource with the standard (S) pricing tier.

Setting up

Create environment variables

Your application must be authenticated to send API requests. For production, use a secure way of storing and accessing your credentials. In this example, you will write your credentials to environment variables on the local machine running the application.

To set the environment variable for your Language resource key, open a console window, and follow the instructions for your operating system and development environment.

  • To set the LANGUAGE_KEY environment variable, replace your-key with one of the keys for your resource.
  • To set the LANGUAGE_ENDPOINT environment variable, replace your-endpoint with the endpoint for your resource.

Important

We recommend Microsoft Entra ID authentication with managed identities for Azure resources to avoid storing credentials with your applications that run in the cloud.

Use API keys with caution. Don't include the API key directly in your code, and never post it publicly. If using API keys, store them securely in Azure Key Vault, rotate the keys regularly, and restrict access to Azure Key Vault using role based access control and network access restrictions.

For more information about AI services security, see Authenticate requests to Azure AI services.

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

After you add the environment variables, run source ~/.bashrc from your console window to make the changes effective.

Install the client library

After installing Python, you can install the client library with:

pip install azure-ai-language-conversations==1.1.0

Code example

Create a new Python file and copy the below code. Then run the code.

Important

Go to the Azure portal. If the Language resource you created in the Prerequisites section deployed successfully, click the Go to Resource button under Next Steps. You can find your key and endpoint by navigating to your resource's Keys and Endpoint page, under Resource Management.

Important

Remember to remove the key from your code when you're done, and never post it publicly. For production, use a secure way of storing and accessing your credentials like Azure Key Vault. See the Azure AI services security article for more information.


key = "paste-your-key-here"
endpoint = "paste-your-endpoint-here"

import os
from azure.core.credentials import AzureKeyCredential
from azure.ai.language.conversations import ConversationAnalysisClient

client = ConversationAnalysisClient(endpoint, AzureKeyCredential(key))
with client:
    poller = client.begin_conversation_analysis(
        task={
            "displayName": "Analyze conversations from xxx",
            "analysisInput": {
                "conversations": [
                    {
                        "conversationItems": [
                            {
                                "text": "Hello, you’re chatting with Rene. How may I help you?",
                                "id": "1",
                                "role": "Agent",
                                "participantId": "Agent_1",
                            },
                            {
                                "text": "Hi, I tried to set up wifi connection for Smart Brew 300 coffee machine, but it didn’t work.",
                                "id": "2",
                                "role": "Customer",
                                "participantId": "Customer_1",
                            },
                            {
                                "text": "I’m sorry to hear that. Let’s see what we can do to fix this issue. Could you please try the following steps for me? First, could you push the wifi connection button, hold for 3 seconds, then let me know if the power light is slowly blinking on and off every second?",
                                "id": "3",
                                "role": "Agent",
                                "participantId": "Agent_1",
                            },
                            {
                                "text": "Yes, I pushed the wifi connection button, and now the power light is slowly blinking.",
                                "id": "4",
                                "role": "Customer",
                                "participantId": "Customer_1",
                            },
                            {
                                "text": "Great. Thank you! Now, please check in your Contoso Coffee app. Does it prompt to ask you to connect with the machine?",
                                "id": "5",
                                "role": "Agent",
                                "participantId": "Agent_1",
                            },
                            {
                                "text": "No. Nothing happened.",
                                "id": "6",
                                "role": "Customer",
                                "participantId": "Customer_1",
                            },
                            {
                                "text": "I’m very sorry to hear that. Let me see if there’s another way to fix the issue. Please hold on for a minute.",
                                "id": "7",
                                "role": "Agent",
                                "participantId": "Agent_1",
                            },
                        ],
                        "modality": "text",
                        "id": "conversation1",
                        "language": "en",
                    },
                ]
            },
            "tasks": [
                {
                    "taskName": "Issue task",
                    "kind": "ConversationalSummarizationTask",
                    "parameters": {"summaryAspects": ["issue"]},
                },
                {
                    "taskName": "Resolution task",
                    "kind": "ConversationalSummarizationTask",
                    "parameters": {"summaryAspects": ["resolution"]},
                },
            ],
        }
    )

    # view result
    result = poller.result()
    task_results = result["tasks"]["items"]
    for task in task_results:
        print(f"\n{task['taskName']} status: {task['status']}")
        task_result = task["results"]
        if task_result["errors"]:
            print("... errors occurred ...")
            for error in task_result["errors"]:
                print(error)
        else:
            conversation_result = task_result["conversations"][0]
            if conversation_result["warnings"]:
                print("... view warnings ...")
                for warning in conversation_result["warnings"]:
                    print(warning)
            else:
                summaries = conversation_result["summaries"]
                for summary in summaries:
                    print(f"{summary['aspect']}: {summary['text']}")

Output

Issue task status: succeeded
issue: Customer tried to set up wifi connection for Smart Brew 300 coffee machine but it didn't work. No error message.

Resolution task status: succeeded
resolution: Asked customer to check if the Contoso Coffee app prompts to connect with the machine. Customer ended the chat.

Use this quickstart to send text summarization requests using the REST API. In the following example, you will use cURL to summarize documents or text-based customer service conversations.

Tip

You can use Language Studio to try text summarization without needing to write code.

Prerequisites

  • The current version of cURL.
  • Once you have your Azure subscription, <create an AI services resource.
    • You will need the key and endpoint from the resource you create to connect your application to the API. You'll paste your key and endpoint into the code below later in the quickstart.
    • You can use the free pricing tier (Free F0) to try the service, and upgrade later to a paid tier for production.

Setting up

Create environment variables

Your application must be authenticated to send API requests. For production, use a secure way of storing and accessing your credentials. In this example, you will write your credentials to environment variables on the local machine running the application.

To set the environment variable for your Language resource key, open a console window, and follow the instructions for your operating system and development environment.

  • To set the LANGUAGE_KEY environment variable, replace your-key with one of the keys for your resource.
  • To set the LANGUAGE_ENDPOINT environment variable, replace your-endpoint with the endpoint for your resource.

Important

We recommend Microsoft Entra ID authentication with managed identities for Azure resources to avoid storing credentials with your applications that run in the cloud.

Use API keys with caution. Don't include the API key directly in your code, and never post it publicly. If using API keys, store them securely in Azure Key Vault, rotate the keys regularly, and restrict access to Azure Key Vault using role based access control and network access restrictions.

For more information about AI services security, see Authenticate requests to Azure AI services.

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

After you add the environment variables, run source ~/.bashrc from your console window to make the changes effective.

Example request

Note

  • The following BASH exaples use the \ line continuation character. If your console or terminal uses a different line continuation character, use that character.
  • You can find language specific samples on GitHub. To call the API, you need the following information:

Choose the type of summarization you would like to perform, and select one of the tabs below to see an example API call:

Feature Description
Text summarization Use extractive text summarization to produce a summary of important or relevant information within a document.
Conversation summarization Use abstractive text summarization to produce a summary of issues and resolutions in transcripts between customer-service agents, and customers.

Conversation issue and resolution summarization

The following example will get you started with conversation issue and resolution summarization:

  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 instead.
curl -i -X POST $LANGUAGE_ENDPOINT/language/analyze-conversations/jobs?api-version=2023-04-01 \
-H "Content-Type: application/json" \
-H "Ocp-Apim-Subscription-Key: $LANGUAGE_KEY" \
-d \
' 
{
  "displayName": "Conversation Task Example",
  "analysisInput": {
    "conversations": [
      {
        "conversationItems": [
          {
            "text": "Hello, you’re chatting with Rene. How may I help you?",
            "id": "1",
            "role": "Agent",
            "participantId": "Agent_1"
          },
          {
            "text": "Hi, I tried to set up wifi connection for Smart Brew 300 espresso machine, but it didn’t work.",
            "id": "2",
            "role": "Customer",
            "participantId": "Customer_1"
          },
          {
            "text": "I’m sorry to hear that. Let’s see what we can do to fix this issue. Could you please try the following steps for me? First, could you push the wifi connection button, hold for 3 seconds, then let me know if the power light is slowly blinking on and off every second?",
            "id": "3",
            "role": "Agent",
            "participantId": "Agent_1"
          },
          {
            "text": "Yes, I pushed the wifi connection button, and now the power light is slowly blinking.",
            "id": "4",
            "role": "Customer",
            "participantId": "Customer_1"
          },
          {
            "text": "Great. Thank you! Now, please check in your Contoso Coffee app. Does it prompt to ask you to connect with the machine? ",
            "id": "5",
            "role": "Agent",
            "participantId": "Agent_1"
          },
          {
            "text": "No. Nothing happened.",
            "id": "6",
            "role": "Customer",
            "participantId": "Customer_1"
          },
          {
            "text": "I’m very sorry to hear that. Let me see if there’s another way to fix the issue. Please hold on for a minute.",
            "id": "7",
            "role": "Agent",
            "participantId": "Agent_1"
          }
        ],
        "modality": "text",
        "id": "conversation1",
        "language": "en"
      }
    ]
  },
  "tasks": [
    {
      "taskName": "Conversation Task 1",
      "kind": "ConversationalSummarizationTask",
      "parameters": {
        "summaryAspects": ["issue"]
      }
    },
    {
      "taskName": "Conversation Task 2",
      "kind": "ConversationalSummarizationTask",
      "parameters": {
        "summaryAspects": ["resolution"],
        "sentenceCount": 1
      }
    }
  ]
}
'

Only the resolution aspect supports sentenceCount. If you do not specify the sentenceCount parameter, the model will determine the summary's length. Note that sentenceCount is just the approximation of sentence count of output summary, range 1 to 7.

  1. Open a command prompt window (for example: BASH).

  2. Paste the command from the text editor into the command prompt window, then run the command.

  3. Get the operation-location from the response header. The value will look similar to the following URL:

https://<your-language-resource-endpoint>/language/analyze-conversations/jobs/12345678-1234-1234-1234-12345678?api-version=2023-04-01
  1. To get the results of the request, use the following cURL command. Be sure to replace <my-job-id> with the numerical ID value you received from the previous operation-location response header:
curl -X GET $LANGUAGE_ENDPOINT/language/analyze-conversations/jobs/<my-job-id>?api-version=2023-04-01 \
-H "Content-Type: application/json" \
-H "Ocp-Apim-Subscription-Key: $LANGUAGE_KEY"

Conversation issue and resolution summarization example JSON response

{
  "jobId": "02ec5134-78bf-45da-8f63-d7410291ec40",
  "lastUpdatedDateTime": "2022-09-29T17:43:02Z",
  "createdDateTime": "2022-09-29T17:43:01Z",
  "expirationDateTime": "2022-09-30T17:43:01Z",
  "status": "succeeded",
  "errors": [],
  "displayName": "Conversation Task Example",
  "tasks": {
    "completed": 2,
    "failed": 0,
    "inProgress": 0,
    "total": 2,
    "items": [
      {
        "kind": "conversationalSummarizationResults",
        "taskName": "Conversation Task 1",
        "lastUpdateDateTime": "2022-09-29T17:43:02.3584219Z",
        "status": "succeeded",
        "results": {
          "conversations": [
            {
              "summaries": [
                {
                  "aspect": "issue",
                  "text": "Customer wants to connect their Smart Brew 300 to their Wi-Fi. | The Wi-Fi connection didn't work."
                }
              ],
              "id": "conversation1",
              "warnings": []
            }
          ],
          "errors": [],
          "modelVersion": "latest"
        }
      },
      {
        "kind": "conversationalSummarizationResults",
        "taskName": "Conversation Task 2",
        "lastUpdateDateTime": "2022-09-29T17:43:02.2099663Z",
        "status": "succeeded",
        "results": {
          "conversations": [
            {
              "summaries": [
                {
                  "aspect": "resolution",
                  "text": "Asked customer to check if the power light is blinking on and off every second."
                }
              ],
              "id": "conversation1",
              "warnings": []
            }
          ],
          "errors": [],
          "modelVersion": "latest"
        }
      }
    ]
  }
}

Clean up resources

If you want to clean up and remove an Azure AI services subscription, you can delete the resource or resource group. Deleting the resource group also deletes any other resources associated with it.

Next steps