A. Bialkowski, P. Lucey, P. Carr, Y. Yue, S. Sridharan et al., Identifying team style in soccer using formations learned from spatiotemporal tracking data, IEEE ICDM Workshops, pp.9-14, 2014.

P. Lucey, D. Oliver, P. Carr, J. Roth, and I. Matthews, Assessing team strategy using spatiotemporal data, ACM SIGKDD, pp.1366-1374, 2013.

J. Van-haaren, V. Dzyuba, S. Hannosset, and J. Davis, Automatically discovering offensive patterns in soccer match data, International Symposium on Intelligent Data Analysis, pp.286-297, 2015.

L. Gyarmati and X. Anguera, Automatic extraction of the passing strategies of soccer teams, 2015.

V. Vercruyssen, L. D. Raedt, and J. Davis, Qualitative spatial reasoning for soccer pass prediction, CEUR Workshop, vol.1842, 2016.

T. Decroos, J. Van-haaren, and J. Davis, Automatic discovery of tactics in spatio-temporal soccer match data, in ACM SIGKDD. ACM, pp.223-232, 2018.

G. Liu and O. Schulte, Deep reinforcement learning in ice hockey for context-aware player evaluation, 2018.

L. Pappalardo, P. Cintia, P. Ferragina, E. Massucco, D. Pedreschi et al., Playerank: data-driven performance evaluation and player ranking in soccer via a machine learning approach, ACM TIST, vol.10, issue.5, pp.1-27, 2019.

T. Decroos, L. Bransen, J. Van-haaren, and J. Davis, Actions speak louder than goals: Valuing player actions in soccer, ACM SIGKDD, pp.1851-1861, 2019.

L. Bransen and J. Van-haaren, Measuring football players' on-the-ball contributions from passes during games, International Workshop on Machine Learning and Data Mining for Sports Analytics, pp.3-15, 2018.

L. Pappalardo, P. Cintia, A. Rossi, E. Massucco, P. Ferragina et al., A public data set of spatio-temporal match events in soccer competitions, Sci Data, vol.6, issue.1, pp.1-15, 2019.

T. Bergmann, S. Bunk, J. Eschrig, C. Hentschel, M. Knuth et al., Linked soccer data, I-SEMANTICS (Posters & Demos)

. Citeseer, , pp.25-29, 2013.

A. Ekin, A. M. Tekalp, and R. Mehrotra, Automatic soccer video analysis and summarization, IEEE Trans. on IP, vol.12, issue.7, pp.796-807, 2003.

M. Y. Eldib, B. S. Zaid, H. M. Zawbaa, M. El-zahar, and M. El-saban, Soccer video summarization using enhanced logo detection, IEEE ICIP, pp.4345-4348, 2009.

N. Nguyen and A. Yoshitaka, Soccer video summarization based on cinematography and motion analysis, IEEE MMSP, pp.1-6, 2014.

M. Tavassolipour, M. Karimian, and S. Kasaei, Event detection and summarization in soccer videos using bayesian network and copula, IEEE Trans. on CSVT, vol.24, issue.2, pp.291-304, 2014.

T. Liu, Y. Lu, X. Lei, L. Zhang, H. Wang et al., Soccer video event detection using 3d convolutional networks and shot boundary detection via deep feature distance, ICONIP, pp.440-449, 2017.

R. Agyeman, R. Muhammad, and G. S. Choi, Soccer video summarization using deep learning, IEEE MIPR, pp.270-273, 2019.

A. Javed, A. Irtaza, Y. Khaliq, H. Malik, and M. T. Mahmood, Replay and key-events detection for sports video summarization using confined elliptical local ternary patterns and extreme learning machine, Applied Intelligence, pp.1-19, 2019.

D. Corney, C. Martin, and A. Göker, Two sides to every story: Subjective event summarization of sports events using twitter, SoMuS@ ICMR. Citeseer, 2014.

A. Tang and S. Boring, # epicplay: Crowd-sourcing sports video highlights, SIGCHI, pp.1569-1572, 2012.

Y. Huang, C. Shen, and T. Li, Event summarization for sports games using twitter streams, vol.21, pp.609-627, 2018.

E. Mendi, H. B. Clemente, and C. Bayrak, Sports video summarization based on motion analysis, Computers & Electrical Engineering, vol.39, issue.3, pp.790-796, 2013.

K. Tang, Y. Bao, Z. Zhao, L. Zhu, Y. Lin et al., Autohighlight: Automatic highlights detection and segmentation in soccer matches, IEEE Big Data, pp.4619-4624, 2018.

Y. Rui, A. Gupta, and A. Acero, Automatically extracting highlights for tv baseball programs, ACM Multimedia, pp.105-115, 2000.

A. Baijal, J. Cho, W. Lee, and B. Ko, Sports highlights generation bas ed on acoustic events detection: A rugby case study, IEEE ICCE, pp.20-23, 2015.

V. Bettadapura, C. Pantofaru, and I. Essa, Leveraging contextual cues for generating basketball highlights, ACM Multimedia, pp.908-917, 2016.

M. Merler, D. Joshi, Q. Nguyen, S. Hammer, J. Kent et al., Automatic curation of golf highlights using multimodal excitement features, pp.57-65, 2017.

A. Raventos, R. Quijada, L. Torres, and F. Tarrés, Automatic summarization of soccer highlights using audio-visual descriptors, vol.4, p.301, 2015.

P. Shukla, H. Sadana, A. Bansal, D. Verma, C. Elmadjian et al., Automatic cricket highlight generation using event-driven and excitement-based features, IEEE CVPRW, pp.1800-1808, 2018.

K. Zhang, W. Chao, F. Sha, and K. Grauman, Summary transfer: Exemplar-based subset selection for video summarization, IEEE CVPR, pp.1059-1067, 2016.

W. Chu, Y. Song, and A. Jaimes, Video co-summarization: Video summarization by visual co-occurrence, IEEE CVPR, pp.3584-3592, 2015.

R. Panda and A. K. Roy-chowdhury, Collaborative summarization of topic-related videos, IEEE CVPR, 2017.

M. Rochan, L. Ye, and Y. Wang, Video summarization using fully convolutional sequence networks, Proceedings of ECCV, pp.347-363, 2018.

M. Gygli, H. Grabner, and L. Van-gool, Video summarization by learning submodular mixtures of objectives, IEEE CVPR, pp.3090-3098, 2015.

X. Li, B. Zhao, and X. Lu, A general framework for edited video and raw video summarization, IEEE Trans. on IP, vol.26, issue.8, pp.3652-3664, 2017.

M. Rochan and Y. Wang, Video summarization by learning from unpaired data, IEEE CVPR, pp.7902-7911, 2019.

X. He, Y. Hua, T. Song, Z. Zhang, Z. Xue et al., Unsupervised video summarization with attentive conditional generative adversarial networks, ACM Multimedia, pp.2296-2304, 2019.

B. Zhao, X. Li, and X. Lu, Hierarchical recurrent neural network for video summarization, ACM Multimedia, pp.863-871, 2017.

J. Wang, W. Wang, Z. Wang, L. Wang, D. Feng et al., Stacked memory network for video summarization, ACM Multimedia, pp.836-844, 2019.

K. Zhang, W. Chao, F. Sha, and K. Grauman, Video summarization with long short-term memory, ECCV, pp.766-782, 2016.

S. Ren, K. He, R. Girshick, and J. Sun, Faster r-cnn: Towards real-time object detection with region proposal networks, NeurIPS, pp.91-99, 2015.

T. Kong, A. Yao, Y. Chen, and F. Sun, Hypernet: Towards accurate region proposal generation and joint object detection, IEEE CVPR, pp.845-853, 2016.

B. Li, J. Yan, W. Wu, Z. Zhu, and X. Hu, High performance visual tracking with siamese region proposal network, IEEE CVPR, pp.8971-8980, 2018.

H. Xu, A. Das, and K. Saenko, R-c3d: region convolutional 3d network for temporal activity detection, IEEE, pp.5794-5803, 2017.

S. Buch, V. Escorcia, C. Shen, B. Ghanem, and J. C. Niebles, Sst: Single-stream temporal action proposals, IEEE CVPR, pp.2911-2920, 2017.

S. Buch, V. Escorcia, B. Ghanem, L. Fei-fei, and J. C. Niebles, Endto-end, single-stream temporal action detection in untrimmed videos, BMVC, 2017.

M. Sanabria, F. Precioso, and T. Menguy, A deep architecture for multimodal summarization of soccer games, ACMM MMSports'19, pp.16-24, 2019.

X. Wang, Y. Yan, P. Tang, X. Bai, and W. Liu, Revisiting multiple instance neural networks, Pattern Recognition, vol.74, pp.15-24, 2018.

C. Hori, T. Hori, T. Lee, Z. Zhang, B. Harsham et al., Attention-based multimodal fusion for video description, IEEE, pp.4193-4202, 2017.

M. Ilse, J. M. Tomczak, and M. Welling, Attention-based deep multiple instance learning, 2018.