For publishers

ovr.news surfaces evidence-based stories from global news sources. We aim to drive readers to your original work, not replace it. Here's exactly how we handle your content.

What we do with your content

• We read your public RSS feed and, when the excerpt is too short, retrieve the full article text from your website to evaluate and summarize it.
• Our AI creates an original summary in new words. We never copy your text. Scientific and research sources receive a summary format adapted for research findings.
• Article text is processed by open-source models on our own hardware.
• We display a source credibility score from external databases (IDIAP, Media Bias/Fact Check, Wikipedia Perennial Sources). This is not our own rating of your publication.

How we attribute

• Every article prominently shows your publication name and links directly to your original article ("Read on yourdomain.com").
• Article pages set <link rel="canonical"> pointing to your original URL, telling search engines your page is the authoritative version.
• Thumbnail images are loaded directly from your servers. We don't rehost them.

What we respect

• Paywalls and access restrictions. If your site blocks access, we accept that.
• robots.txt directives. We comply with the EU Copyright Directive's text and data mining opt-out mechanism (Article 4).
• No advertising or commercial use of your content. ovr.news is non-commercial with no ads.

Editorial context

Your articles appear in a curated feed that also includes non-article context cards: historical figures, verified progress statistics, and notes about our methodology. These are ovr.news editorial content, visually distinguished from your articles, and always cited to external sources. They are not commentary on your reporting.

Opt out

Want your content removed from ovr.news? Use our contact form and select "Publisher opt-out request". We'll remove your sources within 48 hours. No questions, no hassle.

Full transparency

The scoring filters, dimension definitions, and ranking formula that determine how articles are selected and ordered are public.

• How we rank stories — the full ranking formula, including time decay, corroboration boost, and source diversity multiplier.
• LLM Distillery on GitHub — scoring filter definitions for each lens.

Last updated: April 2026