Just like any other modern industry, news publishing is gradually adopting automation powered by Artificial Intelligence (AI) — by its learning and language processing subfields, in particular. Struggling to monetize their content, publishers experiment with ad formats, diversifying revenue streams by introducing paid subscription, while striving to reduce production costs at the same time — and this is where the AI can do a trick.
The basic principle of AI lies in machine learning that allows computers to process vast amounts of data, and to learn from it without being specifically pre-programmed. First, machines have to rely on a set of rules in order to get sufficient knowledge of how a human would perform a particular task — and then the algorithm is set to go! Below are the most innovative ways of automating content production that are gaining momentum at the biggest news organizations right now:
- Automated reporting
- Reformatting of articles
- Text auto-tagging
- Content translation
- Content moderation
- Chat bots
- Content personalization
- Predictive analytics
- Image recognition and auto-tagging
If you’re a news publisher, there’s no need to hire journalists to cover tons of routine stories — an algorithm can do it for you for free with fewer errors and at better speed. The only requirement is to ‘feed’ a robot with clear structured data that can be parsed into ‘variables’. Some bigger news agencies like Associated Press stepped into generating automated content as early as 2014. The news giant then started producing automated stories on corporate financial results using the Wordsmith platform by Automated Insights. As Philana Patterson, the Assistant Business Editor at AP said at that time, the automation of financial quarterly reports freed up to 20% of editors’ time, so they could focus on other tasks.
By 2016, according to the report by Tow Center for Digital Journalism, leading publishers such as Forbes, ProPublica, The New York Times and Los Angeles Times also started to use AI for content production. However, the technology is still emerging and suited only for the topics where accuracy of data is more important than the quality of writing — i.e. financial reports or breaking news.
AP seem to be amongst the pioneers in this field as well. On average, their reporters used to re-write one article to fit several different channels — all manually. That’s why back in 2016, their internal team, in collaboration with a media startup accelerator Matter Ventures, started a new project — development of software that could automate the re-production of a story for all channels, whether for print or broadcast. First, they built a template upon which text for print was transformed into several variations of a copy for digital by shortening the wordage, making sentences more concise and numbers rounded. After a while, a self-learning algorithm, guided by an editor, managed to gain sufficient knowledge to produce multiple versions of the same text autonomously.
Creating a digital article, journalists normally have to either rely on the pre-programmed automated tagging available in CMS or add tags manually — the latter may end up as a total clutter. However, there are smarter alternatives such as “Editor,” a self-learning interface for text editing implemented by The New York Times that automatically tags text and creates annotation based on information gathered through a set of neural networks.
Most international news outlets strive to win a broader audience across countries and languages — this is where translation and adaptation of the content becomes a challenge. Despite the fact that automated translation software and SaaS like Google Translate have been out there for years, the style of the language and poor localization rarely meets high journalistic standards of the most respected news organizations.
EurActiv.com, a multilingual policy news website, has been experimenting with the automated content translation since its inception, and last year they started using an AI-powered technology by the Latvian company Tilde to streamline their processes. The system analyzes tens of thousands of uploaded stories and their human-made translations to learn the language the site uses and aligns it with the official style guide.