Sports News Generation from Live Webcast Scripts Based on Rules and Templates

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10102)


With the dramatic increase of the live webcast scripts about sports, it is an urgent demand to write and publish a sports news article immediately after a sports game. However, so far, the sports news articles are usually written by human experts or journalists, and the manual writing of sports news is time-consuming and inefficient. This paper describes our system on the sports news generation from live webcast scripts task. On one hand, our system extracts the important events occurring in the time period from the live webcast scripts according to the rules, and on the other hand, our system generates a brief summary from the live webcast scripts about the football matches. According to the characteristic of live webcast scripts, we adopt an approach to sentence extraction and template generation from live webcast scripts. The evaluation results show that our system is feasible in sports news generation from live webcast scripts.


Sports news generation Rules Sentence extraction Sentence ranking 


  1. 1.
    Ji, H., Favre, B., Lin, W., et al.: Open-domain multi-document summarization via information extraction: challenges and prospects. Theor. Appl. Nat. Lang. Process. 177–201 (2013). doi:10.1007/978-3-642-28569-1_9
  2. 2.
    Wang, M., Tang, X.: Extract summarization using concept-obtained and hybrid parallel genetic algorithm. In: Natural Computation, pp. 662–664 (2012)Google Scholar
  3. 3.
    Kumar, N., Srinathan, K., Varma, V.: A knowledge induced graph-theoretical model for extract and abstract single document summarization. In: Gelbukh, A. (ed.) CICLing 2013. LNCS, vol. 7817, pp. 408–423. Springer, Heidelberg (2013). doi:10.1007/978-3-642-37256-8_34 CrossRefGoogle Scholar
  4. 4.
    Zhu, J., Wang, C., He, X., et al.: Tag-oriented document summarization. In: Proceedings of the 18th ACM International Conference on World Wide Web, pp. 1195–1196 (2009). doi:10.1145/1526709.1526925
  5. 5.
    Hirao, T., Yoshida, Y., Nishino, M., et al.: Single-document summarization as a tree knapsack problem. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1515–1520 (2013)Google Scholar
  6. 6.
    Liu, M., Wang, L., Nie, L.: Weibo-oriented chinese news summarization via multi-feature combination. In: Li, J., Ji, H., Zhao, D., Feng, Y. (eds.) NLPCC 2015. LNCS (LNAI), vol. 9362, pp. 581–589. Springer, Heidelberg (2015). doi:10.1007/978-3-319-25207-0_55 CrossRefGoogle Scholar
  7. 7.
    Tran, Q., Hwang, D., Lee, O., et al.: Exploiting character networks for movie summarization. Multimedia Tools Appl. 1–13 (2016). doi:10.1007/s11042-016-3633-6
  8. 8.
    Sadiq, A.T., Ali, Y.H., Fadhil, M.S.M.N.: Text summarization for social network conversation. In: International Conference on Advanced Computer Science Applications and Technologies, pp. 3–18 (2013)Google Scholar
  9. 9.
    Li, J., Zhang, K.: Keyword extraction based on tf-idf for Chinese news document. Wuhan Univ. J. Nat. Sci. 12(5), 917–921 (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyWuhan University of Science and TechnologyWuhanChina
  2. 2.Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial SystemWuhan University of Science and TechnologyWuhanChina
  3. 3.Laboratory of Language Engineering and ComputingGuangdong University of Foreign StudiesGuangzhouChina

Personalised recommendations