What is summarization?

This documentation contains the following article types:

• Quickstarts are getting-started instructions to guide you through making requests to the service.
• How-to guides contain instructions for using the service in more specific or customized ways.

Text summarization uses natural language processing techniques to generate a summary for documents. There are two supported API approaches to automatic summarization: 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 that aren't verbatim extract sentences from the original document. These features are designed to shorten content that could be considered too long to read.

Key features for text summarization

There are two aspects of text summarization this API provides:

• Extractive summarization: Produces a summary by extracting salient sentences within the document.

  • Multiple extracted sentences: These sentences collectively convey the main idea of the document. They're original sentences extracted from the input document's content.
  • Rank score: The rank score indicates how relevant a sentence is to a document's main topic. Text summarization ranks extracted sentences, and you can determine whether they're returned in the order they appear, or according to their rank.
  • Multiple returned sentences: Determine the maximum number of sentences to be returned. For example, if you request a three-sentence summary extractive summarization returns the three highest scored sentences.
  • Positional information: The start position and length of extracted sentences.

• Abstractive summarization: Generates a summary that doesn't use the same words as in the document, but captures the main idea.

  • Summary texts: Abstractive summarization returns a summary for each contextual input range within the document. A long document can be segmented so multiple groups of summary texts can be returned with their contextual input range.
  • Contextual input range: The range within the input document that was used to generate the summary text.

As an example, consider the following paragraph of text:

"At Microsoft, we are 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 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 enables 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 achieve 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 text 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 is returned. The output is available for retrieval for 24 hours. After this time, the output is purged. Due to multilingual and emoji support, the response can contain text offsets. For more information, see how to process offsets.

If we use the above example, the API might return these summarized sentences:

Extractive summarization:

• "At Microsoft, we are on a quest to advance AI beyond existing techniques, by taking a more holistic, human-centric approach to learning and understanding."
• "We believe XYZ-code enables 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."

Abstractive summarization:

• "Microsoft is taking a more holistic, human-centric approach to learning and understanding. We believe XYZ-code enables us to fulfill our long-term vision: cross-domain transfer learning, spanning modalities and languages. Over the past five years, we achieved human performance on benchmarks in conversational speech recognition."

This documentation contains the following article types:

• Quickstarts are getting-started instructions to guide you through making requests to the service.
• How-to guides contain instructions for using the service in more specific or customized ways.

Key features for conversation summarization

Conversation summarization supports the following features:

• Issue/resolution summarization: A call center specific feature that gives a summary of issues and resolutions in conversations between customer-service agents and your customers.
• Chapter title summarization: Segments a conversation into chapters based on the topics discussed in the conversation, and gives suggested chapter titles of the input conversation.
• Recap: Summarizes a conversation into a brief paragraph.
• Narrative summarization: Generates detail call notes, meeting notes or chat summaries of the input conversation.
• Follow-up tasks: Gives a list of follow-up tasks discussed in the input conversation.

As an example, consider the following example conversation:

Agent: "Hello, you're chatting with Rene. How may I help you?"

Customer: "Hi, I tried to set up wifi connection for Smart Brew 300 espresso machine, but it didn't work."

Agent: "I'm sorry to hear that. Let's see what we can do to fix this issue. Could you push the wifi connection button, hold for 3 seconds, then let me know if the power light is slowly blinking?"

Customer: "Yes, I pushed the wifi connection button, and now the power light is slowly blinking."

Agent: "Great. Thank you! Now, please check in your Contoso Coffee app. Does it prompt to ask you to connect with the machine?"

Customer: "No. Nothing happened."

Agent: "I see. Thanks. Let's try if a factory reset can solve the issue. Could you please press and hold the center button for 5 seconds to start the factory reset."

Customer: "I've tried the factory reset and followed the above steps again, but it still didn't work."

Agent: "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."

Conversation summarization feature would simplify the text as follows:

This documentation contains the following article types:

• Quickstarts are getting-started instructions to guide you through making requests to the service.
• How-to guides contain instructions for using the service in more specific or customized ways.

Document summarization uses natural language processing techniques to generate a summary for documents. There are two supported API approaches to automatic summarization: extractive and abstractive.

A native document refers to the file format used to create the original document such as Microsoft Word (docx) or a portable document file (pdf). Native document support eliminates the need for text preprocessing prior to using Azure AI Language resource capabilities. Currently, native document support is available for both AbstractiveSummarization and ExtractiveSummarization capabilities.

Currently Text Summarization supports the following native document formats:

For more information, see Use native documents for language processing

Custom Summarization enables users to build custom AI models to summarize unstructured text, such as contracts or novels. By creating a Custom Summarization project, developers can iteratively label data, train, evaluate, and improve model performance before making it available for consumption. The quality of the labeled data greatly impacts model performance. To simplify building and customizing your model, the service offers a custom web portal that can be accessed through the Language studio. You can easily get started with the service by following the steps in this quickstart.

• Summarization takes text for analysis. For more information, see Data and service limits in the how-to guide.
• Summarization works with various written languages. For more information, see language support.

• Conversation summarization takes structured text for analysis. For more information, see data and service limits.
• Conversation summarization accepts text in English. For more information, see language support.

• Summarization takes text for analysis. For more information, see Data and service limits in the how-to guide.
• Summarization works with various written languages. For more information, see language support.