This post is co-written with Aurélien Capdecomme and Bertrand d’Aure from 20 Minutes.
With 19 million monthly readers, 20 Minutes is a major player in the French media landscape. The media organization delivers useful, relevant, and accessible information to an audience that consists primarily of young and active urban readers. Every month, nearly 8.3 million 25–49-year-olds choose 20 Minutes to stay informed. Established in 2002, 20 Minutes consistently reaches more than a third (39 percent) of the French population each month through print, web, and mobile platforms.
As 20 Minutes’s technology team, we’re responsible for developing and operating the organization’s web and mobile offerings and driving innovative technology initiatives. For several years, we have been actively using machine learning and artificial intelligence (AI) to improve our digital publishing workflow and to deliver a relevant and personalized experience to our readers. With the advent of generative AI, and in particular large language models (LLMs), we have now adopted an AI by design strategy, evaluating the application of AI for every new technology product we develop.
One of our key goals is to provide our journalists with a best-in-class digital publishing experience. Our newsroom journalists work on news stories using Storm, our custom in-house digital editing experience. Storm serves as the front end for Nova, our serverless content management system (CMS). These applications are a focus point for our generative AI efforts.
In 2023, we identified several challenges where we see the potential for generative AI to have a positive impact. These include new tools for newsroom journalists, ways to increase audience engagement, and a new way to ensure advertisers can confidently assess the brand safety of our content. To implement these use cases, we rely on Amazon Bedrock.
Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon Web Services (AWS) through a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI.
This blog post outlines various use cases where we’re using generative AI to address digital publishing challenges. We dive into the technical aspects of our implementation and explain our decision to choose Amazon Bedrock as our foundation model provider.
Identifying challenges and use cases
Today’s fast-paced news environment presents both challenges and opportunities for digital publishers. At 20 Minutes, a key goal of our technology team is to develop new tools for our journalists that automate repetitive tasks, improve the quality of reporting, and allow us to reach a wider audience. Based on this goal, we have identified three challenges and corresponding use cases where generative AI can have a positive impact.
The first use case is to use automation to minimize the repetitive manual tasks that journalists perform as part of the digital publishing process. The core work of developing a news story revolves around researching, writing, and editing the article. However, when the article is complete, supporting information and metadata must be defined, such as an article summary, categories, tags, and related articles.
While these tasks can feel like a chore, they are critical to search engine optimization (SEO) and therefore the audience reach of the article. If we can automate some of these repetitive tasks, this use case has the potential to free up time for our newsroom to focus on core journalistic work while increasing the reach of our content.
The second use case is how we republish news agency dispatches at 20 Minutes. Like most news outlets, 20 Minutes subscribes to news agencies, such as the Agence France-Presse (AFP) and others, that publish a feed of news dispatches covering national and international news. 20 Minutes journalists select stories relevant to our audience and rewrite, edit, and expand on them to fit the editorial standards and unique tone our readership is used to. Rewriting these dispatches is also necessary for SEO, as search engines rank duplicate content low. Because this process follows a repeatable pattern, we decided to build an AI-based tool to simplify the republishing process and reduce the time spent on it.
The third and final use case we identified is to improve transparency around the brand safety of our published content. As a digital publisher, 20 Minutes is committed to providing a brand-safe environment for potential advertisers. Content can be classified as brand-safe or not brand-safe based on its appropriateness for advertising and monetization. Depending on the advertiser and brand, different types of content might be considered appropriate. For example, some advertisers might not want their brand to appear next to news content about sensitive topics such as military conflicts, while others might not want to appear next to content about drugs and alcohol.
Organizations such as the Interactive Advertising Bureau (IAB) and the Global Alliance for Responsible Media (GARM) have developed comprehensive guidelines and frameworks for classifying the brand safety of content. Based on these guidelines, data providers such as the IAB and others conduct automated brand safety assessments of digital publishers by regularly crawling websites such as 20minutes.fr and calculating a brand safety score.
However, this brand safety score is site-wide and doesn’t break down the brand safety of individual news articles. Given the reasoning capabilities of LLMs, we decided to develop an automated per-article brand safety assessment based on industry-standard guidelines to provide advertisers with a real-time, granular view of the brand safety of 20 Minutes content.
Our technical solution
At 20 Minutes, we’ve been using AWS since 2017, and we aim to build on top of serverless services whenever possible.
The digital publishing frontend application Storm is a single-page application built using Amazon Simple Storage Service (Amazon S3) and Amazon CloudFront. Our CMS backend Nova is implemented using Amazon API Gateway and several AWS Lambda functions. Amazon DynamoDB serves as the primary database for 20 Minutes articles. New articles and changes to existing articles are captured using DynamoDB Streams, which invokes processing logic in AWS Step Functions and feeds our search service based on Amazon OpenSearch.
We integrate Amazon Bedrock using AWS PrivateLink, which allows us to create a private connection between our Amazon Virtual Private Cloud (VPC) and Amazon Bedrock without traversing the public internet.
When working on articles in Storm, journalists have access to several AI tools implemented using Amazon Bedrock. Storm is a block-based editor that allows journalists to combine multiple blocks of content, such as title, lede, text, image, social media quotes, and more, into a complete article. With Amazon Bedrock, journalists can use AI to generate an article summary suggestion block and place it directly into the article. We use a single-shot prompt with the full article text in context to generate the summary.
Storm CMS also gives journalists suggestions for article metadata. This includes recommendations for appropriate categories, tags, and even in-text links. These references to other 20 Minutes content are critical to increasing audience engagement, as search engines rank content with relevant internal and external links higher.
To implement this, we use a combination of Amazon Comprehend and Amazon Bedrock to extract the most relevant terms from an article’s text and then perform a search against our internal taxonomic database in OpenSearch. Based on the results, Storm provides several suggestions of terms that should be linked to other articles or topics, which users can accept or reject.
News dispatches become available in Storm as soon as we receive them from our partners such as AFP. Journalists can browse the dispatches and select them for republication on 20minutes.fr. Every dispatch is manually reworked by our journalists before publication. To do so, journalists first invoke a rewrite of the article by an LLM using Amazon Bedrock. For this, we use a low-temperature single-shot prompt that instructs the LLM not to reinterpret the article during the rewrite, and to keep the word count and structure as similar as possible. The rewritten article is then manually edited by a journalist in Storm like any other article.
To implement our new brand safety feature, we process every new article published on 20minutes.fr. Currently, we use a single shot prompt that includes both the article text and the IAB brand safety guidelines in context to get a sentiment assessment from the LLM. We then parse the response, store the sentiment, and make it publicly available for each article to be accessed by ad servers.
Lessons learned and outlook
When we started working on generative AI use cases at 20 Minutes, we were surprised at how quickly we were able to iterate on features and get them into production. Thanks to the unified Amazon Bedrock API, it’s easy to switch between models for experimentation and find the best model for each use case.
For the use cases described above, we use Anthropic’s Claude in Amazon Bedrock as our primary LLM because of its overall high quality and, in particular, its quality in recognizing French prompts and generating French completions. Because 20 Minutes content is almost exclusively French, these multilingual capabilities are key for us. We have found that careful prompt engineering is a key success factor and we closely adhere to Anthropic’s prompt engineering resources to maximize completion quality.
Even without relying on approaches like fine-tuning or retrieval-augmented generation (RAG) to date, we can implement use cases that deliver real value to our journalists. Based on data collected from our newsroom journalists, our AI tools save them an average of eight minutes per article. With around 160 pieces of content published every day, this is already a significant amount of time that can now be spent reporting the news to our readers, rather than performing repetitive manual tasks.
The success of these use cases depends not only on technical efforts, but also on close collaboration between our product, engineering, newsroom, marketing, and legal teams. Together, representatives from these roles make up our AI Committee, which establishes clear policies and frameworks to ensure the transparent and responsible use of AI at 20 Minutes. For example, every use of AI is discussed and approved by this committee, and all AI-generated content must undergo human validation before being published.
We believe that generative AI is still in its infancy when it comes to digital publishing, and we look forward to bringing more innovative use cases to our platform this year. We’re currently working on deploying fine-tuned LLMs using Amazon Bedrock to accurately match the tone and voice of our publication and further improve our brand safety analysis capabilities. We also plan to use Bedrock models to tag our existing image library and provide automated suggestions for article images.
Why Amazon Bedrock?
Based on our evaluation of several generative AI model providers and our experience implementing the use cases described above, we selected Amazon Bedrock as our primary provider for all our foundation model needs. The key reasons that influenced this decision were:
- Choice of models: The market for generative AI is evolving rapidly, and the AWS approach of working with multiple leading model providers ensures that we have access to a large and growing set of foundational models through a single API.
- Inference performance: Amazon Bedrock delivers low-latency, high-throughput inference. With on-demand and provisioned throughput, the service can consistently meet all of our capacity needs.
- Private model access: We use AWS PrivateLink to establish a private connection to Amazon Bedrock endpoints without traversing the public internet, ensuring that we maintain full control over the data we send for inference.
- Integration with AWS services: Amazon Bedrock is tightly integrated with AWS services such as AWS Identity and Access Management (IAM) and the AWS Software Development Kit (AWS SDK). As a result, we were able to quickly integrate Bedrock into our existing architecture without having to adapt any new tools or conventions.
Conclusion and outlook
In this blog post, we described how 20 Minutes is using generative AI on Amazon Bedrock to empower our journalists in the newsroom, reach a broader audience, and make brand safety transparent to our advertisers. With these use cases, we’re using generative AI to bring more value to our journalists today, and we’ve built a foundation for promising new AI use cases in the future.
To learn more about Amazon Bedrock, start with Amazon Bedrock Resources for documentation, blog posts, and more customer success stories.
About the authors
Aurélien Capdecomme is the Chief Technology Officer at 20 Minutes, where he leads the IT development and infrastructure teams. With over 20 years of experience in building efficient and cost-optimized architectures, he has a strong focus on serverless strategy, scalable applications and AI initiatives. He has implemented innovation and digital transformation strategies at 20 Minutes, overseeing the complete migration of digital services to the cloud.
Bertrand d’Aure is a software developer at 20 Minutes. An engineer by training, he designs and implements the backend of 20 Minutes applications, with a focus on the software used by journalists to create their stories. Among other things, he is responsible for adding generative AI features to the software to simplify the authoring process.
Dr. Pascal Vogel is a Solutions Architect at Amazon Web Services. He collaborates with enterprise customers across EMEA to build cloud-native solutions with a focus on serverless and generative AI. As a cloud enthusiast, Pascal loves learning new technologies and connecting with like-minded customers who want to make a difference in their cloud journey.
