Text summarization guide for iOS

The MediaPipe Text Summarizer task lets you identify the most important information in a text and generate a shorter version while maintaining the original context meaning. These instructions show you how to use the Text Summarizer in iOS apps.

For more information about the capabilities, models, and configuration options of this task, see the Overview.

Code example

The Text Summarizer iOS example app demonstrates the API on a physical iOS device or simulator.

You can use the app as a starting point for your own iOS app, or refer to it when modifying an existing app. You can refer to the Text Summarizer example code on GitHub.

Setup

This section describes key steps for setting up your development environment and code projects to use Text Summarizer on iOS. For general information on setting up your development environment for using MediaPipe tasks, including platform version requirements, see the Setup guide for iOS.

Dependencies

Text Summarizer uses the MediaPipeTasksText library, which can be installed using Swift Package Manager (SPM). The library is compatible with Swift apps and does not require any additional language-specific setup.

To add the MediaPipeTasksText library to your Xcode project:

1. With your project open in Xcode, select File > Add Package Dependencies....
2. In the search field in the upper right, enter the repository URL: https://github.com/google-ai-edge/mediapipe
3. Select the mediapipe package and choose your dependency rule (for example, Up to Next Major Version or the master branch).
4. Click Add Package, then select MediaPipeTasksText and add it to your application target.

If you use a Package.swift file, add the MediaPipe package and MediaPipeTasksText target dependency using the following code:

dependencies: [
    .package(
        url: "https://github.com/google-ai-edge/mediapipe",
        branch: "master"
    ),
],
targets: [
    .target(
        name: "MyTextSummarizerApp",
        dependencies: [
            .product(name: "MediaPipeTasksText", package: "mediapipe"),
        ]
    ),
]

For more information on configuring dependencies, refer to the Setup guide for iOS.

Model

The MediaPipe Text Summarizer task requires a trained model that is compatible with this task. For more information on available trained models for Text Summarizer, see the task overview Models section.

Select and download a LiteRT model, and add it to your project directory using Xcode. For instructions on how to add files to your Xcode project, refer to Managing files and folders in your Xcode project.

Use the BaseOptions.modelAssetPath property to specify the path to the model in your app bundle.

Create the task

You can create the Text Summarizer task by calling one of its initializers. The TextSummarizer(options:) initializer accepts values for the configuration options.

The following code demonstrates how to build and configure this task.

import MediaPipeTasksText

guard let modelPath = Bundle.main.path(forResource: "summarizer",
                                       ofType: "litertlm") else { return }

let options = TextSummarizerOptions()
options.baseOptions.modelAssetPath = modelPath
options.mode = .tldr

let textSummarizer = try TextSummarizer(options: options)

Configuration options

This task has the following configuration options for iOS apps:

Run the task

To run the summarization inference on the input text, you can use the summarize(text:) method of TextSummarizer for blocking inference, or the summarizeStreaming(text:completion:) method for asynchronous callbacks.

Synchronous

let result = try textSummarizer.summarize(text: text)

Streaming (Asynchronous callbacks)

try textSummarizer.summarizeStreaming(text: text) { streamResult, error in
    if let error = error {
        print("Error: \(error)")
        return
    }

    guard let streamResult = streamResult else { return }
    print(streamResult.chunk)

    if streamResult.done {
        print("Completed summarizing!")
    }
}

Handle and display results

Upon running inference synchronously, the Text Summarizer returns an instance containing the complete string using the summary property.

let finalOutput = result.summary

When running in stream mode, the callback continually outputs a TextSummarizerStreamResult, containing .chunk (the next text partial), and a .done boolean determining stream completion status.

Clean Up

Don't forget to explicitly close the summarizer engine instances when finished.

try textSummarizer.close()