SUMMARIZATION AND TIMELINE GENERATION OVER EVOLUTIONARY TWEET STREAMS
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Abstract
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
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