MMSUM Digital Twins: A Multi-view Multi-modality Summarization Framework for Sporting Events

Abstract

Sporting events generate a massive amount of traffic on social media with live moment-to-moment accounts as any given situation unfolds. The generated data are intensified by fans feelings, reactions, and subjective opinions towards what happens during the event, all of which are based on their individual points of view. Analyzing and summarizing this data will generate a comprehensive overview of the event in terms of how the event evolves and how fans react and view the event based on their perspectives. Previously, most of the summarization works ignore fan reactions and subjective opinions, and focus primarily on generating an objective-view summary. We believe that an effective and useful summary should consider human reactions, sentiment, and point of view, as opposed to simply describing what happens during the event. Accordingly, in this work, we propose MMSUM Digital Twins: a summarization framework that is capable of generating a multi-view multi-modal summary for sporting events in real-time. The proposed digital twins-based framework consists of four main components: sub-event recognition which detects the event’s key moments, tweet categorization, which determines which team the tweets’ writers support and assigns tweets to their teams, sentiment analysis to track fans’ state of mind, and image popularity prediction for selecting representative images. Furthermore, the MMSUM employs a visual-filtering model to address the issue of noisy images that inundate social media, compromising the summarization quality. We leverage the knowledge of sport fans to evaluate the generated multi-view summarization through an online user study. The experiment results confirm the effectiveness of our proposed approach for summarizing sporting events by considering multimedia data, sentiment, and subjective views of the event.