Google News Article Summary Pop-Up

When a user clicks a news article in Google News, a pop-up shows a Gemini AI-generated summary from the original source, with a link to the full article.

App name / Client

Google News

My Role

Researcher

Industry

Own Interest

Platform

Web/Mobile

project image

Introduction:

This case study details a project focused on increasing next-day retention for Google News by improving personalization, credibility, and efficiency in news consumption. My role was to define the product vision, align stakeholders, and oversee feature development to drive engagement. The project ran from February 2025 to July 2025.

Project Name:

Google News - Improvement

Role:

Product Manager (Researcher)

Problem Statement:

Users were encountering irrelevant news recommendations, leading to lower engagement and retention. Trust in news sources was low due to the prevalence of opinion-based content, and long-form articles were overwhelming for time-constrained users.

Strategy and Roadmap:

The strategy focused on three core improvements:

  1. Personalization: AI-powered Smart Feeds to prioritize relevant topics.
    1. Credibility: Fact-Check Badge and Trusted Source Toggle.
      1. Efficiency: AI-generated Smart Summaries and Audio Summaries for quick consumption.

        The roadmap included research, feature design, phased implementation, and impact measurement to ensure smooth adoption.

        Research and Validation User research, including surveys and interviews, identified key pain points around relevance and trust. A/B testing validated the impact of personalization features, and prototype feedback sessions refined the user experience before launch.

        Product Development Process Following Agile methodology, we conducted two-week sprints, tracking progress in Jira and documenting insights in Confluence. Prioritization was guided by user impact and feasibility, ensuring high-value features were delivered early.

        Execution and Delivery: Development proceeded in phases, with incremental feature releases. The team overcame data integration challenges by optimizing AI models and refining feed algorithms based on real-time engagement data.

        Challenges and Mitigations

        • Data Accuracy: Ensured relevance through continuous model training and feedback loops.
          • Balancing Personalization & Diversity: Implemented guardrails to prevent over-filtering of diverse perspectives.
            • User Trust: Introduced credibility labels and transparency features to build confidence in news sources.

              Conclusion and Future Roadmap:

              The improvements in retention and engagement validate the impact of personalized and credible news delivery. Future iterations will enhance AI explainability, deeper integration with Google Assistant for voice-based summaries, and expanded multilingual support to cater to a broader audience. This project serves as a foundation for scaling AI-driven content personalization across other Google products.