Key result

Reduced post-event highlight processing time by 87.5% while maintaining 90% event-selection accuracy.

Why it matters

  • Sports audiences expect near-instant recap content across social and broadcast channels.
  • Manual highlight editing pipelines struggle to scale with content demand.

Approach

  • Combined lightweight neural components with rule-based prioritization over multimodal inputs.
  • Generated timestamped candidate clips immediately after match completion.

Results

  • Demonstrated production-ready acceleration from raw feeds to publishable highlight packages.
  • Validated real-world applicability through deployment in an active platform workflow.

Abstract

In the era of social media and instantaneous content consumption, the demand for quick post-event sports highlights has emerged, requiring systems to deliver compelling summaries in a minimal turnaround time. This paper introduces an AI-driven framework designed to streamline the creation of match recaps by leveraging real-time data acquisition and efficient post-processing. The system analyzes multi-modal inputs —including live game statistics, audio-visual feeds, and contextual cues— to automatically identify key moments (e.g., goals, pivotal plays) as they occur. By integrating lightweight neural models and rule-based prioritization, it generates timestamped clips immediately after the match concludes, significantly reducing manual editing effort. The solution not only supports human editors by providing pre-curated material but also enables fully automated highlight production for platforms requiring instant content delivery. Evaluations on soccer and basketball matches demonstrate the system’s ability to cut post-event processing time by 87.5% while maintaining 90% accuracy in event selection compared to manual curation. The work underscores the potential of hybrid AI systems to bridge real-time analytics with post-production workflows, offering scalability across sports and media formats. The generated highlights are already being published on a production platform (https://tiivii.gal), demonstrating real-world applicability.