SUMMARIZATION AND TIMELINE GENERATION OVER EVOLUTIONARY TWEET STREAMS
DOI:
https://doi.org/10.17605/OSF.IO/32N85Keywords:
Continuous summarization, tweet clustering, summary, Tweet stream, TCVAbstract
In recent years, number of users are being interested in the Social Networking site as well as micro blogging
websites for example Twitter, Facebook etc. In a single day Twitter counts Tweets over a 500 million. The very
complex part is in this system is to control the real time applications sharing, keeping as well as managing like
large data. Because of huge amount of data generated by the user, it goes from different concentrating problems
like noisy as well as frequent information. By the researchers view, Querying as well as retrieval of like large
information, it is very essential and one of the critical problems. Previous system tends to only work on the
static as well as the limited information. Number of previous systems were tried to solve this problem and
additionally given different solution over the problem. Summarization is a procedure consists of a text document
in like a manner that short summary created through implementing the essential keywords of the original
document. In this paper, we have tendency to propose the new method that builds the appropriate content-based
summery in limited period of time. Our proposed system is also time efficient as compared with previous
systems. We proposed a TCV-Rank summarization technique for generating online summaries and historical
summaries of arbitrary time durations. Which monitors summary based/volume-based variations to produce
timelines automatically from tweet streams; we design an effective topic evolution detection method. Our
experiments on large-scale real tweets demonstrate the efficiency and effectiveness of our framework
Downloads
References
L. Vanderwende, H. Suzuki, C. Brockett, and A. Nenkova, Beyond SumBasic: Task-focused
summarization with sentence simplification and lexical expansion, Information Processing
Management, vol. 43, no. 6, pp. 16061618, 2007.
G. Erkan and D. Radev, Lexrank: graph-based centrality as salience in text summarization, Journal of
Artificial Intelligence Research, vol. 22, pp. 457480, 2004.
R. Mihalcea and P. Tarau, TextRank: Bringing order into texts, in EMNLP. Barcelona: ACL, 2004, pp.
ArkaitzZubiaga, DamianoSpina, Enrique Amigó and Julio Gonzalo, Towards Real-Time
Summarization of Scheduled Events from Twitter Streams , in Proc. 23rd ACM Conf. Hypertext Social
Media,2012.
C. Shen, F. Liu, F. Weng, and T. Li, A participant-based approach for event summarization using
twitter streams, in Proc. Human Lang. Technol. Annu. Conf. North Amer. Chapter Assoc. Comput.
Linguistics, 2013, pp. 1152–1162.
C. C. Aggarwal, J. Han, J. Wang, and P. S. Yu, A framework for clustering evolving data streams, in
Proc. 29th Int. Conf. Very Large Data Bases, 2003, pp. 8192.
T. Zhang, R. Ramakrishnan, and M. Livny, BIRCH: An efficient data clustering method for very large
databases, in Proc. ACM SIGMOD Int. Conf. Manage. Data, 1996, pp. 103114.
Mitsumasa Kubo, RyoheiSasano, HiroyaTakamura, and Manabu Okumura, Generating Live Sports
Updates from Twitter by Finding Good Reporters, in IEEE,2013.
David Inouye, Jugal K. Kalita, Comparing Twitter Summarization Algorithms for Multiple Post
Summaries, IEEE Trans. Knowl. Data Eng., 23(8):12001214, 2011
Zhenhua Wang, LidanShou, Ke Chen, On Summarization and Timeline Generation for Evolutionary
Tweet Streams, ieee transactions on knowledge and data engineering, vol. 27, no. 5, may 2015
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
