Generating Objective Summaries of Sports Matches Using Social Media

Koleejan, Chahine; Takamura, Hiroya; Okumura, Manabu

doi:10.1145/3350546.3352546

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…Koleejan, Takamura and Okumura [11] also propose a summarization approach using social media streams, including as one of its steps a burst detection approach. They presented tests showing the efficiency of the proposed approach based on ten matches from the English Premier League and Champions League in 2017.…”

Section: A Analyzing Sports Videos Through Highlights Detectionmentioning

confidence: 99%

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PeakVis: a Visual Analysis Tool for Social Network Data and Video Broadcasts

Sanvido

Kurtz

Teixeira

et al. 2021

2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC)

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Television and live internet broadcasts coexist with a stream of social network commentary, engaging audiences in more complex ways than traditional ratings can represent, demanding convergent tools to analyze such contemporary media. In this work, we present PeakVis, an interactive tool that syncs a video recording with a responsive line graph representing the total number of tweets from each moment, the top messages, a dynamic word cloud, and a semantic graph showing word correlation. Thus, this novel interactive approach allows the analysis of broadcast highlights identified through Twitter posts peaks. We explored this freely available tool's applicability by analyzing two different case studies: the season finale of a reality show and the final episode of a telenovela. We could observe that it allows us to quickly identify the most relevant segments and grasp the discussed subjects in-depth through the obtained results, showing its value in broadcast analysis.

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Section: A Analyzing Sports Videos Through Highlights Detectionmentioning

confidence: 99%

“…D3js: https://d3js.org/11 Plotly: https://plotly.com/ 12 wordcloud2.js: https://wordcloud2-js.timdream.org…”

mentioning

confidence: 99%

PeakVis: a Visual Analysis Tool for Social Network Data and Video Broadcasts

Sanvido

Kurtz

Teixeira

et al. 2021

2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC)

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Response Generation by Jointly Modeling Personalized Linguistic Styles and Emotions

Sun

Chun

Song

et al. 2022

ACM Trans. Multimedia Comput. Commun. Appl.

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Natural language generation (NLG) has been an essential technique for various applications, like XiaoIce and Siri, and engaged increasing attention recently. To improve the user experience, several emotion-aware NLG methods have been developed to generate responses coherent with a pre-designated emotion (e.g., the positive or negative). Nevertheless, existing methods cannot generate personalized responses as they frequently overlook the personalized linguistic style. Apparently, different human responsers tend to have different linguistic styles. Inspired by this, in this work, we focus on a novel research theme of personalized emotion-aware NLG ( PENLG ), whereby the generated responses should be coherent with the linguistic style of a pre-designated responser and emotion. In particular, we study PENLG under a scenario of generating personalized emotion-aware response for social media post. Yet it faces certain research challenges: (1) the user linguistic styles are implicit and complex by nature, and hence it is hard to learn their representations; and (2) linguistic styles and emotions are usually expressed in different manners in a response, and thus how to convey them properly in the generated responses is not easy. Toward this end, we present a novel scheme of PENLG, named CRobot, which consists of a personalized emotion-aware response generator and two discriminators, i.e., general discriminator and personalized emotion-aware discriminator . To be more specific, the post-based and avatar-based user linguistic style modeling methods are incorporated into the encoder-decoder–based generator, while the discriminators are devised to ensure that the generated response is fluent and consistent with both the emotion and the linguistic style of the user. Different from the traditional adversarial networks, we embed adversarial learning under the umbrella of reinforcement learning. In this way, the response generation problem can be tackled by the generator taking a sequence of actions on selecting the proper word of each timestep for output. To justify our model, we construct a large-scale response generation dataset based on Twitter, consisting of 6,763 tweets with a corresponding 1,461,713 response created by 153,664 users. Extensive experiments demonstrate that CRobot surpasses the state-of-the-art baselines regarding both subjective and objective evaluation.

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Towards Subjective Multimedia Summarization Framework for Sporting Event in the Context of Digital Twins

Aloufi¹

2020

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Generating Objective Summaries of Sports Matches Using Social Media

Cited by 3 publications

References 11 publications

PeakVis: a Visual Analysis Tool for Social Network Data and Video Broadcasts

PeakVis: a Visual Analysis Tool for Social Network Data and Video Broadcasts

Response Generation by Jointly Modeling Personalized Linguistic Styles and Emotions

Towards Subjective Multimedia Summarization Framework for Sporting Event in the Context of Digital Twins

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