Multimodal AI-Based Summarization and Storytelling for Soccer on Social Media

Abstract

Soccer is not only a beloved global sport but also a significant industry that generates substantial revenue through sponsorship, merchandise, broadcast rights, and ticket sales. In today’s digital age, the way fans consume soccer content is evolving. A growing number of fans now prefer to watch or read game highlights rather than full matches, a shift driven by busy lifestyles and a preference for brief, digestible content formats. This change is facilitated by digital platforms that cater to the demand for quick and engaging soccer insights. In this changing landscape, the role of Artificial Intelligence (AI) is becoming increasingly crucial. AI technology has the potential to streamline the production of game highlights by automatically detecting and compiling key moments such as goals and significant plays. This not only saves time and reduces errors but also enhances the consistency of media output, aligning production with consumer preferences for quick and accessible content. Building on this premise, this thesis aims to develop an AI-driven method specifically designed to automatically generate multi-tweets thread summarizing soccer goals. This innovative approach allows fans to visualise the essence of game highlights without needing to watch the full event. It also offers soccer clubs a cost-effective strategy to engage with fans on social media platforms like Twitter, potentially boosting long-term revenue in this evolving business era. To achieve these objective, SoccerSum leverages a multimodal AI-based system, developed and extensively tested through objective and two rounds of subjective evaluations. SoccerSum combines video, audio, and text modalities to generate concise, engaging summaries for goal events specifically designed for Twitter. Evaluation results demonstrate that SoccerSum excels in delivering accurate name entities, integrating pitch elements effectively, and using language that resonates with users.

Keywords: AI, Machine Learning (ML), Social Media, Content Generation, Generative AI