Abstract
In this paper, we proposed an approach to automatically generate timeline summarization for sub-event discussions related to a query event without supervised learning. In order to select event-related sentences, we designed a two-stage method to extract representative entity terms in the event-related discussions and filter out most of the sentences semantically un-related to the query event. A rule-based method was applied to extract sentences which describing sub-events. After that, the discussions are assigned to the corresponding sub-events according to the semantic relatedness measure. Finally, according to the occurring time of each sub-event, the timeline summarization is organized. We evaluated the performance of the proposed method on the real-world datasets. The experiment results showed that each processing step perform effectively. Especially, most noise sentences could be filtered by the proposed method. Moreover, the final timeline summarization graded by users is proven to be useful to well understand the discussion trend of a sub-event.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Avvenuti, M., Meletti, C., Cresci, S., Tesconi, M., Marchetti, A.: EARS (earthquake alert and report system): a real time decision support system for earthquake crisis management. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014, pp. 1749–1758 (2014)
Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes twitter users: real-time event detection by social sensors. In: Proceedings of the 19th International Conference on World Wide Web, WWW 2010, pp. 851–860 (2010)
Chae, J., Thom, D., Ebert, D.S., Bosch, H., Ertl, T., Jang, Y., Maciejewski, R.: Spatiotemporal social media analytics for abnormal event detection and examination using seasonal-trend decomposition. In: IEEE, Visual Analytics Science and Technology (VAST), pp. 143–152 (2012)
Pohl, D., Bouchachia, A., Hellwagner, H.: Automatic sub-event detection in emergency management using social media. In: Proceedings of the 21st International Conference on World Wide Web (short paper), WWW 2012, pp. 683–686 (2012)
Tuan, T., Calaudia, N., Nattiya, K., Ujwal, G., Avishek, A.: Balancing novelty and salience: adaptive learning to rank entities for timeline summarization of high-impact events. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, CIKM 2015, pp. 1201–1210 (2015)
Abhik, D., Toshniwal, D.: Sub-event detection during nature hazards using features of social media data. In: Proceedings of the 22nd International Conference on World Wide Web, WWW 2013, pp. 783–788 (2013)
Shou, L., Wang, Z., Chen, K., Chen, G.: Sumblr: continuous summarization of evolving tweet streams. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2013, pp. 533–542 (2013)
Paik, J.H., Oard, D.W.: A fixed-point method for weighting terms in verbose informational queries. In: Proceedings of the 23rd ACM International Conference on Information and Knowledge Management, CIKM 2014, pp. 131–140 (2014)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient Estimation of Word Representations in Vector Space (2013). arXiv:13013781v3
Siersdorfer, S., Kemkes, P., Ackermann, H., Zerr, S.: Who with Whom and How? – Extracting large social networks using search engines. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, CIKM 2015, pp. 1491–1500 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Wang, H., Koh, JL. (2017). Timeline Summarization for Event-Related Discussions on a Chinese Social Media Platform. In: Benferhat, S., Tabia, K., Ali, M. (eds) Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science(), vol 10350. Springer, Cham. https://doi.org/10.1007/978-3-319-60042-0_64
Download citation
DOI: https://doi.org/10.1007/978-3-319-60042-0_64
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60041-3
Online ISBN: 978-3-319-60042-0
eBook Packages: Computer ScienceComputer Science (R0)