@misc{17199,
  author = {Sushant Gautam},
  title = {AI-based Soccer Game Summarization: From Video Highlights to Dynamic Text Summaries},
  abstract = {Soccer dominates the global sports market, and viewers{\textquoteright} interest in watching videos of soccer matches is ramping up. Globally, there is a huge and constantly increasing amount of soccer game content being generated, including video footage, audio commentary, text metadata, goal and player statistics, scores, and rankings. As a large percentage of audiences prefer to follow only the major highlights of a game, the creation of multimodal (video/audio/text) summaries is of great interest to broadcasters and fans alike. In this regard, it{\textquoteright}s crucial to provide game summaries and highlights of the major game moments. However, creating summaries and annotating events most often necessitates the use of expensive equipment and a significant amount of time-consuming manual labor. Recent advancements in Artificial Intelligence (AI) technology have demonstrated great promise in this context. The purpose of this thesis is to use AI to support an automated pipeline for summarizing soccer matches. With Natural Language Processing (NLP) tools and heuristics, the emphasis is on creating comprehensive game summaries in textual form with variable length constraints, based on raw game multimedia (e.g., video and audio streams) and, where appropriate, easily accessible game meta-data. A longformer model has been fine-tuned to output a game summary for a given textual input of game captions. This work also explores the use of game audio in prioritizing game events from a summarization perspective. In particular, the Root Mean Square (RMS) audio intensity score has been extracted and used to extract the event priority to be included in the summary.},
  year = {2022},
  journal = {Tribhuvan University},
}