参考本文开始使用客户端库和 REST API 进行文本摘要。 按照以下步骤使用示例代码挖掘文本：
参考文档 | 库源代码 | 包 (NuGet) | 其他示例
- Azure 订阅 - 创建试用订阅
- Visual Studio IDE
- 拥有 Azure 订阅后，请在 Azure 门户中创建语言资源，以获取密钥和终结点。 部署后，单击“转到资源”。
- 需要从创建的资源获取密钥和终结点，以便将应用程序连接到 API。 你稍后会在快速入门中将密钥和终结点粘贴到下方的代码中。
- 可以使用免费定价层 (
F0) 试用该服务，然后再升级到付费层进行生产。
- 若要使用“分析”功能，需要标准 (S) 定价层的“语言”资源。
使用 Visual Studio IDE 创建新的 .NET Core 控制台应用。 这会创建包含单个 C# 源文件的“Hello World”项目：program.cs。
右键单击解决方案资源管理器中的解决方案，然后选择“管理 NuGet 包”，以便安装客户端库。 在打开的包管理器中选择“浏览”，搜索 Azure.AI.TextAnalytics。 请务必选中“包括预发行版”。 选择版本 5.2.0-beta.1，然后选择“安装”。 也可使用包管理器控制台。
将以下代码复制到 program.cs 文件。 请记得将 key 变量替换为资源的密钥，并将 endpoint 变量替换为资源的终结点。
重要
转到 Azure 门户。 如果你在“先决条件”部分创建的语言资源部署成功，请单击“后续步骤”下的“转到资源”按钮 。 可以通过导航到资源的“密钥和终结点”页，在“资源管理”下找到你的密钥和终结点。
重要
完成后，请记住将密钥从代码中删除，并且永远不要公开发布该密钥。 对于生产来说，请使用安全的方式存储和访问凭据，例如 Azure Key Vault。 有关详细信息，请参阅 Azure AI 服务安全性一文。
using Azure;
using System;
using Azure.AI.TextAnalytics;
using System.Threading.Tasks;
using System.Collections.Generic;
namespace LanguageDetectionExample
{
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");
// Example method for summarizing text
static async Task TextSummarizationExample(TextAnalyticsClient client)
{
string document = @"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.
In the public preview, 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." ;
// Prepare analyze operation input. You can add multiple documents to this list and perform the same
// operation to all of them.
var batchInput = new List<string>
{
document
};
TextAnalyticsActions actions = new TextAnalyticsActions()
{
ExtractSummaryActions = new List<ExtractSummaryAction>() { new ExtractSummaryAction() }
};
// Start analysis process.
AnalyzeActionsOperation operation = await client.StartAnalyzeActionsAsync(batchInput, actions);
await operation.WaitForCompletionAsync();
// View operation status.
Console.WriteLine($"AnalyzeActions operation has completed");
Console.WriteLine();
Console.WriteLine($"Created On : {operation.CreatedOn}");
Console.WriteLine($"Expires On : {operation.ExpiresOn}");
Console.WriteLine($"Id : {operation.Id}");
Console.WriteLine($"Status : {operation.Status}");
Console.WriteLine();
// View operation results.
await foreach (AnalyzeActionsResult documentsInPage in operation.Value)
{
IReadOnlyCollection<ExtractSummaryActionResult> summaryResults = documentsInPage.ExtractSummaryResults;
foreach (ExtractSummaryActionResult summaryActionResults in summaryResults)
{
if (summaryActionResults.HasError)
{
Console.WriteLine($" Error!");
Console.WriteLine($" Action error code: {summaryActionResults.Error.ErrorCode}.");
Console.WriteLine($" Message: {summaryActionResults.Error.Message}");
continue;
}
foreach (ExtractSummaryResult documentResults in summaryActionResults.DocumentsResults)
{
if (documentResults.HasError)
{
Console.WriteLine($" Error!");
Console.WriteLine($" Document error code: {documentResults.Error.ErrorCode}.");
Console.WriteLine($" Message: {documentResults.Error.Message}");
continue;
}
Console.WriteLine($" Extracted the following {documentResults.Sentences.Count} sentence(s):");
Console.WriteLine();
foreach (SummarySentence sentence in documentResults.Sentences)
{
Console.WriteLine($" Sentence: {sentence.Text}");
Console.WriteLine();
}
}
}
}
}
static async Task Main(string[] args)
{
var client = new TextAnalyticsClient(endpoint, credentials);
await TextSummarizationExample(client);
}
}
}
AnalyzeActions operation has completed
Created On : 9/16/2021 8:04:27 PM +00:00
Expires On : 9/17/2021 8:04:27 PM +00:00
Id : 2e63fa58-fbaa-4be9-a700-080cff098f91
Status : succeeded
Extracted the following 3 sentence(s):
Sentence: The extractive summarization feature in uses natural language processing techniques to locate key sentences in an unstructured text document.
Sentence: This feature is provided as an API for developers.
Sentence: They can use it to build intelligent solutions based on the relevant information extracted to support various use cases.
- Azure 订阅 - 创建试用订阅
- Java 开发工具包 (JDK) 版本 8 或更高版本
- 拥有 Azure 订阅后，请在 Azure 门户中创建语言资源，以获取密钥和终结点。 部署后，单击“转到资源”。
- 需要从创建的资源获取密钥和终结点，以便将应用程序连接到 API。 你稍后会在快速入门中将密钥和终结点粘贴到下方的代码中。
- 可以使用免费定价层 (
F0) 试用该服务，然后再升级到付费层进行生产。
- 若要使用“分析”功能，需要标准 (S) 定价层的“语言”资源。
在首选 IDE 或开发环境中创建 Maven 项目。 然后在项目的 pom.xml 文件中，添加以下依赖项。 可联机找到用于其他生成工具的实现语法。
<dependencies>
<dependency>
<groupId>com.azure</groupId>
<artifactId>azure-ai-textanalytics</artifactId>
<version>5.2.0-beta.1</version>
</dependency>
</dependencies>
创建名为 Example.java 的 Java 文件。 打开该文件，并复制以下代码。 请记得将 key 变量替换为资源的密钥，并将 endpoint 变量替换为资源的终结点。
重要
转到 Azure 门户。 如果你在“先决条件”部分创建的语言资源部署成功，请单击“后续步骤”下的“转到资源”按钮 。 可以通过导航到资源的“密钥和终结点”页，在“资源管理”下找到你的密钥和终结点。
重要
完成后，请记住将密钥从代码中删除，并且永远不要公开发布该密钥。 对于生产来说，请使用安全的方式存储和访问凭据，例如 Azure Key Vault。 有关详细信息，请参阅 Azure AI 服务安全性一文。
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 {
private static String KEY = "replace-with-your-key-here";
private static String ENDPOINT = "replace-with-your-endpoint-here";
public static void main(String[] args) {
TextAnalyticsClient client = authenticateClient(KEY, ENDPOINT);
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. "
+ "In the public preview, 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());
}
}
});
}
}
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: 156, offset: 0, rank score: 0.980000.
Sentence text: This feature is provided as an API for developers., length: 50, offset: 224, rank score: 0.990000.
Sentence text: They can use it to build intelligent solutions based on the relevant information extracted to support various use cases., length: 120, offset: 275, rank score: 1.000000.
- Azure 订阅 - 创建试用订阅
- 最新版本的 Node.js。
- 拥有 Azure 订阅后，请在 Azure 门户中创建语言资源，以获取密钥和终结点。 部署后，单击“转到资源”。
- 需要从创建的资源获取密钥和终结点，以便将应用程序连接到 API。 你稍后会在快速入门中将密钥和终结点粘贴到下方的代码中。
- 可以使用免费定价层 (
F0) 试用该服务，然后再升级到付费层进行生产。
- 若要使用“分析”功能，需要标准 (S) 定价层的“语言”资源。
在控制台窗口（例如 cmd、PowerShell 或 Bash）中，为应用创建一个新目录并导航到该目录。
mkdir myapp
cd myapp
运行 npm init 命令以使用 package.json 文件创建一个 node 应用程序。
npm init
安装 NPM 包：
npm install --save @azure/ai-text-analytics@5.2.0-beta.1
打开该文件，并复制以下代码。 请记得将 key 变量替换为资源的密钥，并将 endpoint 变量替换为资源的终结点。
重要
转到 Azure 门户。 如果你在“先决条件”部分创建的语言资源部署成功，请单击“后续步骤”下的“转到资源”按钮 。 可以通过导航到资源的“密钥和终结点”页，在“资源管理”下找到你的密钥和终结点。
重要
完成后，请记住将密钥从代码中删除，并且永远不要公开发布该密钥。 对于生产来说，请使用安全的方式存储和访问凭据，例如 Azure Key Vault。 有关详细信息，请参阅 Azure AI 服务安全性一文。
"use strict";
const { TextAnalyticsClient, AzureKeyCredential } = require("@azure/ai-text-analytics");
const key = '<paste-your-key-here>';
const endpoint = '<paste-your-endpoint-here>';
// Authenticate the client with your key and endpoint
const textAnalyticsClient = new TextAnalyticsClient(endpoint, new AzureKeyCredential(key));
// Example method for summarizing text
async function summarization_example(client) {
const documents = [`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.
In the public preview, 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.`];
console.log("== Analyze Sample For Extract Summary ==");
const actions = {
extractSummaryActions: [{ modelVersion: "latest", orderBy: "Rank", maxSentenceCount: 5 }],
};
const poller = await client.beginAnalyzeActions(documents, actions, "en");
poller.onProgress(() => {
console.log(
`Number of actions still in progress: ${poller.getOperationState().actionsInProgressCount}`
);
});
console.log(`The analyze actions operation created on ${poller.getOperationState().createdOn}`);
console.log(
`The analyze actions operation results will expire on ${poller.getOperationState().expiresOn}`
);
const resultPages = await poller.pollUntilDone();
for await (const page of resultPages) {
const extractSummaryAction = page.extractSummaryResults[0];
if (!extractSummaryAction.error) {
for (const doc of extractSummaryAction.results) {
console.log(`- Document ${doc.id}`);
if (!doc.error) {
console.log("\tSummary:");
for (const sentence of doc.sentences) {
console.log(`\t- ${sentence.text}`);
}
} else {
console.error("\tError:", doc.error);
}
}
}
}
}
summarization_example(textAnalyticsClient).catch((err) => {
console.error("The sample encountered an error:", err);
});
== Analyze Sample For Extract Summary ==
The analyze actions operation created on Thu Sep 16 2021 13:12:31 GMT-0700 (Pacific Daylight Time)
The analyze actions operation results will expire on Fri Sep 17 2021 13:12:31 GMT-0700 (Pacific Daylight Time)
- Document 0
Summary:
- They can use it to build intelligent solutions based on the relevant information extracted to support various use cases.
- This feature is provided as an API for developers.
- 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.
- In the public preview, extractive summarization supports several languages.
- Azure 订阅 - 创建试用订阅
- Python 3.x
- 拥有 Azure 订阅后，请在 Azure 门户中创建语言资源，以获取密钥和终结点。 部署后，单击“转到资源”。
- 需要从创建的资源获取密钥和终结点，以便将应用程序连接到 API。 你稍后会在快速入门中将密钥和终结点粘贴到下方的代码中。
- 可以使用免费定价层 (
F0) 试用该服务，然后再升级到付费层进行生产。
- 若要使用“分析”功能，需要标准 (S) 定价层的“语言”资源。
在安装 Python 后，可以通过以下命令安装客户端库：
pip install azure-ai-textanalytics==5.2.0b1
创建新的 Python 文件，并复制以下代码。 请记得将 key 变量替换为资源的密钥，并将 endpoint 变量替换为资源的终结点。
重要
转到 Azure 门户。 如果你在“先决条件”部分创建的语言资源部署成功，请单击“后续步骤”下的“转到资源”按钮 。 可以通过导航到资源的“密钥和终结点”页，在“资源管理”下找到你的密钥和终结点。
重要
完成后，请记住将密钥从代码中删除，并且永远不要公开发布该密钥。 对于生产来说，请使用安全的方式存储和访问凭据，例如 Azure Key Vault。 有关详细信息，请参阅 Azure AI 服务安全性一文。
key = "paste-your-key-here"
endpoint = "paste-your-endpoint-here"
from azure.ai.textanalytics import TextAnalyticsClient
from azure.core.credentials import AzureKeyCredential
# Authenticate the client using your key and endpoint
def authenticate_client():
ta_credential = AzureKeyCredential(key)
text_analytics_client = TextAnalyticsClient(
endpoint=endpoint,
credential=ta_credential)
return text_analytics_client
client = authenticate_client()
# Example method for summarizing text
def sample_extractive_summarization(client):
from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics import (
TextAnalyticsClient,
ExtractSummaryAction
)
document = [
"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. "
"In the public preview, 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. "
]
poller = client.begin_analyze_actions(
document,
actions=[
ExtractSummaryAction(max_sentence_count=4)
],
)
document_results = poller.result()
for result in document_results:
extract_summary_result = result[0] # first document, first result
if extract_summary_result.is_error:
print("...Is an error with code '{}' and message '{}'".format(
extract_summary_result.code, extract_summary_result.message
))
else:
print("Summary extracted: \n{}".format(
" ".join([sentence.text for sentence in extract_summary_result.sentences]))
)
sample_extractive_summarization(client)
Summary extracted:
The extractive summarization feature uses natural language processing techniques to locate key sentences in an unstructured text 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.
- 最新版本的 cURL。
- 拥有 Azure 订阅后，请在 Azure 门户中创建语言资源，以获取密钥和终结点。 部署后，单击“转到资源”。
- 需要从创建的资源获取密钥和终结点，以便将应用程序连接到 API。 你稍后会在快速入门中将密钥和终结点粘贴到下方的代码中。
- 可以使用免费定价层 (
F0) 试用该服务，然后再升级到付费层进行生产。
备注
- 以下 BASH 示例使用
\行继续符。 如果你的控制台或终端使用不同的行继续符，请使用该字符。 - 可以在 GitHub 上找到特定于语言的示例。
- 访问 Azure 门户，并找到之前在先决条件部分中创建的语言资源的密钥和终结点。 它们将位于资源的“密钥和终结点”页的“资源管理”下。 然后，将以下代码中的字符串替换为你的密钥和终结点。 若要调用 API，需要以下信息：
| 参数 (parameter) | 说明 |
|---|---|
-X POST <endpoint> |
指定用于访问 API 的终结点。 |
-H Content-Type: application/json |
用于发送 JSON 数据的内容类型。 |
-H "Ocp-Apim-Subscription-Key:<key> |
指定用于访问 API 的密钥。 |
-d <documents> |
包含要发送的文档的 JSON。 |
以下 cURL 命令从 BASH shell 中执行。 请使用自己的资源名称、资源密钥和 JSON 值编辑这些命令。
- 将命令复制到文本编辑器中。
- 必要时在命令中进行如下更改：
- 将值
<your-language-resource-key>替换为你的值。 - 将请求 URL 的第一部分 (
<your-language-resource-endpoint>) 替换为你的终结点 URL。
- 将值
- 打开命令提示符窗口。
- 将文本编辑器中的命令粘贴到命令提示符窗口，然后运行命令。
curl -i -X POST https://your-text-analytics-endpoint-here/text/analytics/v3.2-preview.1/analyze \
-H "Content-Type: application/json" \
-H "Ocp-Apim-Subscription-Key: your-key-here" \
-d \
'
{
"analysisInput": {
"documents": [
{
"language": "en",
"id": "1",
"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 is 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 pretrained 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 multisensory 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."
}
]
},
"tasks": {
"extractiveSummarizationTasks": [
{
"parameters": {
"model-version": "latest",
"sentenceCount": 3,
"sortBy": "Offset"
}
}
]
}
}
'
从响应头获取 operation-location。 值将如下 URL 所示：
https://your-resource.cognitiveservices.azure.cn/text/analytics/v3.2-preview.1/analyze/jobs/12345678-1234-1234-1234-12345678
要获取请求的结果，请使用以下 cURL 命令。 请务必将 my-job-id 替换为从之前的 operation-location 响应头中收到的数值 ID 值：
curl -X GET https://your-text-analytics-endpoint-here/text/analytics/v3.2-preview.1/analyze/jobs/my-job-id \
-H "Content-Type: application/json" \
-H "Ocp-Apim-Subscription-Key: your-key-here"
{
"jobId":"da3a2f68-eb90-4410-b28b-76960d010ec6",
"lastUpdateDateTime":"2021-08-24T19:15:47Z",
"createdDateTime":"2021-08-24T19:15:28Z",
"expirationDateTime":"2021-08-25T19:15:28Z",
"status":"succeeded",
"errors":[
],
"displayName":"NA",
"tasks":{
"completed":1,
"failed":0,
"inProgress":0,
"total":1,
"extractiveSummarizationTasks":[
{
"lastUpdateDateTime":"2021-08-24T19:15:48.0011189Z",
"taskName":"ExtractiveSummarization_latest",
"state":"succeeded",
"results":{
"documents":[
{
"id":"1",
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"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.",
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{
"text":"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).",
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{
"text":"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.",
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"modelVersion":"2021-08-01"
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}
}
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