Transformer-based Live Update Generation for Soccer Matches from Microblog Posts (2023)

Abstract: It has been known to be difficult to generate adequate sports updates from a sequence of vast amounts of diverse live tweets, although the live sports viewing experience with tweets is gaining the popularity. In this paper, we focus on soccer matches and work on building a system to generate live updates for soccer matches from tweets so that users can instantly grasp a match’s progress and enjoy the excitement of the match from raw tweets. Our proposed system is based on a large pre-trained language model and incorporates a mechanism to control the number of updates and a mechanism to reduce the redundancy of duplicate and similar updates.

## introduction when a sports match is broadcast, twitter users often enjoy sharing the status of the match or their opinions. for example, during a televised soccer match, many users post tweets that include information about how goals were scored or their thoughts on a certain play, and it is possible to roughly understand a match's progress by reading these tweets. however, because of the diverse nature of tweets, ranging from informative to purely emotive content, it can be challenging to quickly grasp a match's progress. in this study, we focus on soccer matches and work on building a system to generate live sports updates from tweets so that users can instantly grasp a match's progress. generation of live sports updates from tweets can be considered a type of multi-document summarization @xcite . much research exists on multi-document summarization, including recent studies that aim to generate highquality summarization by capturing the hierarchical structure of documents @xcite . more recently, pre-trained language models such as bart and primera have also been used @xcite 2022). however, these methods are not easy to apply to this task because they do not account for time series data. time-series-aware update generation methods can be divided into two approaches depending on the generation timing. one approach determines the timing according to the number of posts per unit time @xcite @xcite . while this approach can accurately capture main events, it tends to miss less significant events that have fewer related tweets. another approach, which generates updates at regular intervals @xcite , can generate exhaustive updates but tends to produce redundancies by mentioning the same event at different times after it occurs. in this paper, we build a pre-trained language model-based system to generate live updates for soccer matches. in particular, we propose to incorporate a mechanism to control the number of updates and a mechanism to mitigate redundancy in the system. figure 1 shows an overview of our system. it uses the pre-trained language model textto-text transfer transformer (t5) @xcite , for which the inputs are tweets related to a specific match and the outputs are updates at certain times. if there is no appropriate update, "nan" is output. to control the number of updates, it uses a binary classifier to determine whether an update should be generated at each time, and to mitigate redundancy, it leverages preceding updates. our system is based on an abstractive model using t5. we also apply the approach that generates the update every minute. as a preliminary experiment regarding the generation timing, we tried another approach, which is detecting events such as goals or substitutions by monitoring the increasing number of tweets and generating updates at those detected points. however, we confirmed that some events such as close shot or good defensive plays could not be detected. for this reason, our models typically generate one update per minute and output "nan" if they determine that it is better not to output any update. figure 2 (a) shows the architecture of our baseline model (m base ). it generates updates every minute without referring to preceding updates; thus, updates may be redundant if an event is mentioned repeatedly over a long period. in addition, "nan" is more frequently generated unless a highly probable word is predicted at the beginning of the output, which may result in too many "nan." to address these problems, in this study, we propose a model that incorporates a classifier to determine whether to output "nan" and a mechanism to mitigate redundancy by incorporating preceding updates. ## experiment to confirm the proposed method's effectiveness, we compared the performance of the baseline model, three proposed models, and two oracle models. ## conclusion in this paper, we have investigated the update generation of soccer matches from tweets. we presented models based on t5 and attempted to incorporate a classifier to control the number of updates and preceding updates to reduce redundancy. we confirmed that our method can achieve high performance by considering preceding updates. however, the generation of redundant updates was not completely suppressed, and this remains a challenge for implementing an update generation system with higher performance. in the future, we would like to investigate update generation for other sports.