What is summarization?
Important
Our preview region, Sweden Central, showcases our latest and continually evolving LLM fine tuning techniques based on GPT models. You are welcome to try them out with a Language resource in the Sweden Central region.
Conversation summarization is only available using:
- REST API
- Python
- C#
Summarization is one feature offered by Azure AI Language, which is a combination of generative Large Language models and task-optimized encoder models that offer summarization solutions with higher quality, cost efficiency, and lower latency. Use this article to learn more about this feature, and how to use it in your applications.
Out of the box, the service provides summarization solutions for three types of genre, plain texts, conversations, and native documents. Text summarization only accepts plain text blocks, and conversation summarization accept conversational input, including various speech audio signals in order for the model to effectively segment and summarize, and native document can directly summarize for documents in their native formats, such as Words, PDF, etc.
Tip
Try out Summarization in AI Foundry portal, where you can utilize a currently existing Language Studio resource or create a new AI Foundry resource in order to use this service.
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.
These features are designed to shorten content that could be considered too long to read.
Key features for text summarization
Text summarization uses natural language processing techniques to generate a summary for plain texts, which can be from a document or a conversation, or any texts. There are two approaches of summarization this API provides:
Extractive summarization: Produces a summary by extracting salient sentences within the document, together the positioning information of these sentences.
- 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 the 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. 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 with concise, coherent sentences or words that aren't verbatim extract sentences from the original document.
- Summary texts: Abstractive summarization returns a summary for each contextual input range. A long input 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 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 summaries:
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."
Get started with summarization
To use summarization, you submit for analysis and handle the API output in your application. Analysis is performed as-is, with no added customization to the model used on your data. There are two ways to use summarization:
| Development option | Description |
|---|---|
| Language studio | Language Studio is a web-based platform that lets you try entity linking with text examples without an Azure account, and your own data when you sign up. For more information, see the Language Studio website or language studio quickstart. |
| REST API or Client library (Azure SDK) | Integrate text summarization into your applications using the REST API, or the client library available in various languages. For more information, see the summarization quickstart. |
Input requirements and service limits
- 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.
Reference documentation and code samples
As you use text summarization in your applications, see the following reference documentation and samples for Azure AI Language:
| Development option / language | Reference documentation | Samples |
|---|---|---|
| C# | C# documentation | C# samples |
| Java | Java documentation | Java Samples |
| JavaScript | JavaScript documentation | JavaScript samples |
| Python | Python documentation | Python samples |
Responsible AI
An AI system includes not only the technology, but also the people who use it, the people affected by it, and the deployment environment. Read the transparency note for summarization to learn about responsible AI use and deployment in your systems. For more information, see the following articles: