The Summarizer API helps Bright Sites and Terra create engaging article summaries  |  Blog  |  Chrome for Developers
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Built-in AI
The Summarizer API helps Bright Sites and Terra create engaging article summaries
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Mari Viana
LinkedIn
Thierno Thiam
X
GitHub
LinkedIn
Published: May 15, 2025
The public relies on news publishers to inform them of local, national, and
international events, as well as share their thoughtful perspectives. In
today's fast-paced environment, article summaries are a key strategy to condense
complex information into accessible snippets, encouraging readers to explore
further. Learn how Bright Sites and Terra approach this strategy using the
Summarizer API.
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The Summarizer API lets you generate different types
of summaries in varied lengths and formats, such as sentences, paragraphs,
bullet point lists, and more. This API, like many of the
built-in AI APIs, use large language models to perform
inference.
Bright Sites adds personalized article summaries to The Standard
Bright Sites' leading news publishing
platform, Flow, powers over 150
publications. By integrating the Summarizer API into its AI-powered CMS,
Bright Sites enables The Standard, London's
iconic news brand, to offer personalized summaries of articles to its readers.
This encourages users to engage with articles and recirculate to other articles
on The Standard.
"Working with Gemini's cutting edge models in Chrome is bringing together The Standard's 198 years of heritage of keeping our readers in the know with new technical innovations for our busy audience who value having our trusted journalism at their fingertips. Enabling readers to quickly check the news using summaries built on their own devices is just one of the ways in which we're reimagining The Standard and building new reader-focused products around their digital habits."
— Jack Riley, Chief Digital Officer, The Standard
Offering client-side AI summarization enabled The Standard to generate
personalized summaries without incurring additional business cost. Doing so
client-side, using data points like summary style or location, would allow users
to continue reading even when they lose internet access. This is a frequent
occurrence for many users who travel on the London Underground with their
laptop.
First, they check for support and availability of the model and the API.
// Check if the device supports built-in AI
// Trigger the model download if not yet available, on capable devices
export const deviceCheck = async () => {
const availability = await Summarizer.availability();
if (!availability || availability === 'unavailable') {
return {
summarizationAvailable: false,
message:
"AI summarization tools are not supported on this device" +
"or the appropriate permissions are not set.",
}
}
if (availability === 'downloadable') {
const shouldDownload = window.confirm(
`This page contains an AI summary, using an AI model provided by your
browser. Downloading the model, which could be multiple gigabytes in size,
is required to view the summary. Would you like to download the model?`);
if (!shouldDownload) {
return {
summarizationAvailable: false,
message: "User declined installation.",
}
}
// Trigger an installation
Summarizer.create();
return {
summarizationAvailable: false,
message: "Installing in the background. This may take a few minutes...",
}
}
if (availability === 'available') {
return {
summarizationAvailable: true,
message: "Ready for use.",
}
}
}
The following function defines a generic summarizer that, in the
future, could use another on-device model or a server-side model.
/**
* Define the summarizer.
**/
export const aiSummarize = async (textToSummarize, options) => {
const availableSummarizationTools = getAvailableAiSummarizationTools()
if (availableSummarizationTools.has('builtInAi') && options?.builtInAI) {
// Generate the built-in AI summarizer and abort signal
const summarizer = await createBuiltInAISummarizer(options.builtInAi.options)
return await summarizer.summarize(textToSummarize, {
signal: options.builtInAi.signal,
})
}
throw new Error(
'AI summarization tools are not supported on this device or browser.',
)
}
The Standard stores the readers' summary preference in IndexedDB to offer personalized summary.
/**
* Log preferences in IndexDB for personalization
**/
abortController.current = new AbortController()
const preferencesDB = new PreferencesDB()
const summarization = await aiSummarize(articleContent, {
clientSideAI: {
options: await preferencesDB.getCreatesummarizerPreferences(),
signal: abortController.current.signal,
},
})
Terra provides journalists an editable summary for articles in Portuguese
Terra is one of the largest content portals from
Brazil, offering entertainment, news, and sports with more than 50 million
unique visitors per month. Terra added the Summarizer API and the Translator
API to their content management system (CMS) to help journalists instantly
summarize news stories in Portuguese. Journalists can then make a few edits
for stylistic or accuracy purposes and publish the co-created summary, making
it readily available to all readers.
While Terra's CMS already uses a server-side LLM, the team explored client-side
AI as a distinct approach with potential new benefits. They found the
Summarizer API in Chrome provided
comparable quality to their server-side implementation. The client-side solution
yielded positive results when used in conjunction with the
Translator API.
Terra adopted Chrome's implementation of built-in AI because of key advantages.
Client-side AI offered cost savings and simplified data governance. While the
team encountered some challenges, notably around
managing content window limitations with the
Summarizer API, they were able to overcome these through careful implementation
practices.
Initially, Terra faced challenges in determining what
summary types and shared context were
best equipped to address their needs. Through experimentation, they discovered
that clear and useful English summaries were crucial for producing a similar
quality output in Portuguese with the Translator API. The
built-in AI playground
was instrumental in navigating these challenges, as Terra could quickly test
their ideas without refactoring their code each time.
The following sample demonstrates how Terra invokes the Summarizer API and
alerts users when it's unavailable.
async function summarizerByBuiltInAI(text) {
if (!(Summarizer)) {
//Alert users in Portuguese that "Summarizer API is not available"
cms_alert(ALERT_TYPE_ERROR, "Summarizer API não está disponível.")
return null
}
try {
const availability = await Summarizer.availability();
if (availability !== 'available') {
cms_alert(ALERT_TYPE_ERROR, "Summarizer API não está disponível.")
return null
}
const summaryContext = "Avoid jargon, use correct grammar, focus on clarity," +
"and ensure the user can grasp the articles purpose," +
"without needing to open the original content.";
const options = {
sharedContext: summaryContext,
type: 'teaser',
format: 'plain-text',
length: 'long',
}
if (availability === 'available') {
const summarizer = await Summarizer.create(options);
return await summarizer.summarize(text, {
context: summaryContext
})
}
// return the download of the Summarizer Model
if(availability === 'downloadable'){
return await Summarizer.create();
}
} catch (error) {
//EN: "Error using the Summarizer API"
cms_alert(ALERT_TYPE_ERROR, "Erro ao usar o Summarizer API.");
console.error("Erro ao usar o Summarizer API:", error);
return null
}
}
In addition, Terra used the summarizer in conjunction with the Translator API,
to translate the original Portuguese article's title, subtitle, and body text to
English. This translated version is processed by the Summarizer API to generate
the summary, and then translated back into Portuguese. This ensures the user
receives the summarized content in the application's language.
async function translateTextByBuiltInAI(text, sourceLanguage, targetLanguage) {
if (!('translation' in self && 'createTranslator' in self.translation)) {
return null
}
try {
const translator = await Translator.create({
sourceLanguage,
targetLanguage,
})
return await translator.translate(text)
} catch (error) {
throw error
}
}
const text = `Title: ${contentTitle};\n\n Sub-title: ${contentSubtitle};\n\n Article content: ${plainText}.`;
const canTranslate = await Translator.availability({
sourceLanguage: 'pt',
targetLanguage: 'en',
})
if (canTranslate !== 'available') {
if (canTranslate === 'downloadable') {
try {
await Translator.create({
sourceLanguage: 'pt',
targetLanguage: 'en',
})
//EN: "Language download completed successfully."
cms_alert(ALERT_TYPE_OK, "Download do idioma concluído com sucesso.");
} catch (downloadError) {
//EN: "Error downloading the language required for translation."
cms_alert(ALERT_TYPE_ERROR, "Erro ao realizar download do idioma necessário para tradução.");
return
}
} else {
//EN: "Translation is not available or not ready."
cms_alert(ALERT_TYPE_ERROR, "A tradução não está disponível ou não está pronta.");
return
}
}
const translatedText = await translateTextByBuiltInAI(text, 'pt', 'en')
const summarizedText = await summarizerByBuiltInAI(translatedText)
const translatedBackText = await translateTextByBuiltInAI(summarizedText, 'en', 'pt')
Terra's successful integration of built-in AI APIs demonstrates the significant
potential of client-side AI to enhance content management workflows. With the
Summarizer and Translator APIs, Terra has empowered its journalists, improved
efficiency, and is well-positioned to deliver enhanced user experiences
across platforms.
Best practices
Note: You can find these best practices in both this blog post and
the case study on the Summarizer API and user reviews.
If the review input exceeds the token limit, follow these mitigations:
Use a smaller sample (such as the most recent 4 reviews) to the API. This
helps generate faster results. Refer to our guidance on
scaling client-side summarization.
The QuotaExceededError provides more information about the requested tokens
in the input. The summarizer object has an inputQuota property that
indicates the API's token limit. This allows for real-time feedback and
disabling functionality if the input exceeds the limit.
You may want to consider a hybrid approach, to ensure a seamless experience
for all users. The first time a built-in AI API is called, the browser must
download the model.
Miravia used a server-side model to provide an initial summary, while the
model was downloading. Once the built-in model was ready, the site switched
to performing inference client-side.
You should always strive to create a friendly and communicative interface:
Implement a progress bar
for model downloads and mitigate response delays.
Consider transparency about the model download. Bright Sites notified users
about the model download to allow for transparency and consent of resource
usage. That way, users could accept or decline before proceeding.
Conclusions and recommendations
Bright Sites and Terra's examples demonstrate the value of Summarizer API in
enhancing content accessibility and reader engagement. By using this client-side
API, these platforms improved reading experience and personalization, without
additional business cost and with simplified data governance. Just like the
Summarizer API, all of the built-in AI APIs
enable practical client-side AI.
Wondering how the Summarizer API can help with other use cases? We've also
shared how the Summarizer API helps redBus and Miravia create helpful user review summaries.
Are you building something new with these APIs? Share it with us at
@ChromiumDev on X or
Chromium for Developers on LinkedIn.
Resources
Learn more about Summarizer API.
Start using Built-in APIs on Chrome.
Read the Prompt API case study on empowering bloggers.
Read the Translation and Language Detector case study.
Read how the Summarizer API helps redBus and Miravia create helpful user review summaries.
Acknowledgements
Thank you to Guilherme Moser and
Fernando Fischer from Terra,
Aline Souza from CWI,
Brian Alford,
Keval Patel,
Jack Riley and Brightsites'
Engineering team,
Swetha Gopalakrishnan,
Alexandra Klepper,
Thomas Steiner and
Kenji Baheux for helping to write and
review this document.
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2025-05-15 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-05-15 UTC."],[],[]]
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