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dc.contributor.authorGautam, Sushant
dc.contributor.authorMidoglu, Cise
dc.contributor.authorSabet, Saeed
dc.contributor.authorKshatri, Dinesh Baniya
dc.contributor.authorHalvorsen, Pål
dc.date.accessioned2023-03-28T07:58:21Z
dc.date.available2023-03-28T07:58:21Z
dc.date.created2023-01-06T12:22:59Z
dc.date.issued2022
dc.identifier.isbn978-1-4503-9493-2
dc.identifier.urihttps://hdl.handle.net/11250/3060647
dc.description.abstractSoccer 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.en_US
dc.language.isoengen_US
dc.publisherAssociation for Computing Machinery (ACM)en_US
dc.relation.ispartofNarSUM '22: Proceedings of the 1st Workshop on User-centric Narrative Summarization of Long Videos
dc.relation.ispartofseriesMM: International Multimedia Conference;MM '22: The 30th ACM International Conference on Multimedia
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleSoccer Game Summarization using Audio Commentary, Metadata, and Captionsen_US
dc.typeConference objecten_US
dc.description.versionpublishedVersionen_US
cristin.ispublishedtrue
cristin.fulltextoriginal
dc.identifier.doihttps://doi.org/10.1145/3552463.3557019
dc.identifier.cristin2101990
dc.source.pagenumber13-22en_US


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