Soccer Game Summarization using Audio Commentary, Metadata, and Captions

Abstract

Soccer is one of the most popular sports globally, and the amount of soccer-related content worldwide, including video footage, audio commentary, team/player statistics, scores, and rankings, is enormous and rapidly growing. Consequently, the generation of multimodal summaries is of tremendous interest for broadcasters and fans alike, as a large percentage of audiences prefer to follow only the main highlights of a game. However, annotating important events and producing summaries often requires expensive equipment and a lot of tedious, cumbersome, manual labour. In this context, recent developments in Artificial Intelligence (AI) have shown great potential. The goal of this work is to create an automated soccer game summarization pipeline using AI. In particular, our focus is on the generation of complete game summaries in continuous text format with length constraints, based on raw game multimedia, as well as readily available game metadata and captions where applicable, using Natural Language Processing (NLP) tools along with heuristics. We curate and extend a number of soccer datasets, implement an end-to-end pipeline for the automatic generation of text summaries, present our preliminary results from the comparative analysis of various summarization methods within this pipeline using different input modalities, and provide a discussion of open challenges in the field of automated game summarization.