<\/p>\n
We’re delighted to announce that Text Analytics now supports extractive summarization!\nIn general, there are two approaches for automatic text summarization: extractive and abstractive.\nThe Text Analytics API provides extractive summarization starting in version 3.2-preview.1<\/p>\n
Text Analytics for Extractive Summarization is a feature in Azure Text Analytics that produces a text\nsummary by extracting sentences that collectively represent the most important or relevant information\nwithin the original content. This feature is designed to shorten content that could be considered too\nlong to read. Extractive summarization condenses articles, papers, or documents to key sentences.<\/p>\n
Text Analytics for Extractive Summarization supports the following features:<\/p>\n
Next, we will walk through a sample usage of extractive summarization.<\/p>\n
You can find complete samples for Java<\/a>,\nC#<\/a>, and Python<\/a> on our website.<\/p>\n In this section, we will show how to use extractive summarization in Java.\nTo use Text Analytics for Text Extractive Summarization, start with creating a Text Analytics client,\nand then use the client to make a request to the Text Analytics service on the documents input, which\nwill return the analyzed output that includes the features described above.<\/p>\n Create a Text Analytics client,<\/p>\n Prepare a batch of documents as input,<\/p>\n Next, let’s create an extractive summarization action and pass it in a call to Since this operation is long-running, we will call For example, given an article document,<\/p>\n “At Microsoft, we have been on a quest to advance AI beyond existing techniques, by taking a more holistic,\nhuman-centric approach to learning and understanding. As Chief Technology Officer of Azure AI Cognitive\nServices, I have been working with a team of amazing scientists and engineers to turn this quest into a\nreality. In my role, I enjoy a unique perspective in viewing the relationship among three attributes of\nhuman cognition: monolingual text (X), audio or visual sensory signals, (Y) and multilingual (Z). At the\nintersection of all three, there\u2019s magic\u2014what we call XYZ-code as illustrated in Figure 1\u2014a joint\nrepresentation to create more powerful AI that can speak, hear, see, and understand humans better. We\nbelieve XYZ-code will enable us to fulfill our long-term vision: cross-domain transfer learning, spanning\nmodalities and languages. The goal is to have pretrained models that can jointly learn representations to\nsupport a broad range of downstream AI tasks, much in the way humans do today. Over the past five years,\nwe have achieved human performance on benchmarks in conversational speech recognition, machine translation,\nconversational question answering, machine reading comprehension, and image captioning. These five\nbreakthroughs provided us with strong signals toward our more ambitious aspiration to produce a leap in AI\ncapabilities, achieving multisensory and multilingual learning that is closer in line with how humans learn\nand understand. I believe the joint XYZ-code is a foundational component of this aspiration, if grounded\nwith external knowledge sources in the downstream AI tasks.”<\/em><\/p>\n The extracted summary sentences display as,<\/p>\n Text Analytics for Extractive Summarization is a new feature in the Azure Text Analytics service that\nproduces a text summary by extracting sentences that collectively represent the most important or\nrelevant information within the original document. Furthermore, we have shown how to use it in Java by\ncalling This article introduced the Text Analytics library features for extractive summarization analysis<\/a>.<\/p>\n For more information about each language from this article, see the following resources:<\/p>\n <\/p>\n Thank you for reading this Azure SDK blog post!\nWe hope that you learned something new and welcome you to share this post.\nWe are open to Azure SDK blog contributions.\nPlease contact us at azsdkblog@microsoft.com<\/a> with your topic and we’ll get you set up as a guest blogger.<\/p>\n <\/p>\n","protected":false},"excerpt":{"rendered":" This article introduces the new Text Analytics feature for extractive summarization.<\/p>\n","protected":false},"author":56388,"featured_media":1513,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1],"tags":[160,793,792],"class_list":["post-1511","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-azure-sdk","tag-java","tag-preview","tag-text-analytics"],"acf":[],"blog_post_summary":" This article introduces the new Text Analytics feature for extractive summarization.<\/p>\n","_links":{"self":[{"href":"https:\/\/devblogs.microsoft.com\/azure-sdk\/wp-json\/wp\/v2\/posts\/1511","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/devblogs.microsoft.com\/azure-sdk\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/devblogs.microsoft.com\/azure-sdk\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/devblogs.microsoft.com\/azure-sdk\/wp-json\/wp\/v2\/users\/56388"}],"replies":[{"embeddable":true,"href":"https:\/\/devblogs.microsoft.com\/azure-sdk\/wp-json\/wp\/v2\/comments?post=1511"}],"version-history":[{"count":0,"href":"https:\/\/devblogs.microsoft.com\/azure-sdk\/wp-json\/wp\/v2\/posts\/1511\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/devblogs.microsoft.com\/azure-sdk\/wp-json\/wp\/v2\/media\/1513"}],"wp:attachment":[{"href":"https:\/\/devblogs.microsoft.com\/azure-sdk\/wp-json\/wp\/v2\/media?parent=1511"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/devblogs.microsoft.com\/azure-sdk\/wp-json\/wp\/v2\/categories?post=1511"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/devblogs.microsoft.com\/azure-sdk\/wp-json\/wp\/v2\/tags?post=1511"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}TextAnalyticsClient client = new TextAnalyticsClientBuilder()\r\n .credential(new AzureKeyCredential(\"{key}\"))\r\n .endpoint(\"{endpoint}\")\r\n .buildClient();<\/code><\/pre>\nList<String> documents = Arrays.asList(\r\n \"<first document input string your want to analyze>\",\r\n \"<second document input string your want to analyze>\"); <\/code><\/pre>\nbeginAnalyzeActions<\/code>:<\/p>\nSyncPoller<AnalyzeActionsOperationDetail, AnalyzeActionsResultPagedIterable> syncPoller =\r\n client.beginAnalyzeActions(documents,\r\n new TextAnalyticsActions().setExtractSummaryActions(new ExtractSummaryAction()),\r\n \"en\",\r\n null);\r\n\r\nsyncPoller.waitForCompletion();<\/code><\/pre>\ngetFinalResult()<\/code> on the poller to get the results after waiting\nis completed:<\/p>\nsyncPoller.getFinalResult().forEach(actionsResult -> {\r\n System.out.println(\"Extractive Summarization action results:\");\r\n for (ExtractSummaryActionResult actionResult : actionsResult.getExtractSummaryResults()) {\r\n for (ExtractSummaryResult documentResult : actionResult.getDocumentsResults()) {\r\n System.out.println(\"tExtracted summary sentences:\");\r\n for (SummarySentence summarySentence : documentResult.getSentences()) {\r\n System.out.printf(\"tt Sentence text: %s, length: %d, offset: %d, rank score: %f.%n\",\r\n summarySentence.getText(), summarySentence.getLength(), summarySentence.getOffset(), summarySentence.getRankScore());\r\n } \r\n }\r\n }\r\n});<\/code><\/pre>\nExtractive Summarization action results:\r\n Extracted summary sentences:\r\n Sentence 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., length: 160, offset: 0, rank score: 1.000000.\r\n Sentence 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)., length: 192, offset: 324, rank score: 0.958233.\r\n Sentence text: At the intersection of all three, there\u2019s magic\u2014what we call XYZ-code as illustrated in Figure 1\u2014a joint representation to create more powerful AI that can speak, hear, see, and understand humans better., length: 203, offset: 517, rank score: 0.929475.<\/code><\/pre>\nSummary<\/h2>\n
beginAnalyzeActions<\/code>.<\/p>\n\n
Azure SDK Blog Contributions<\/h2>\n
Azure SDK Links<\/h2>\n
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