Survey on Tweet Summarization Approaches

Abstract: Now-a-days, the large amounts of short messages are shared among multiple peoples and data. These short messages are known as tweets on social networking sites and micro blogging sites. The recent observations said that twitter receives over hundreds million tweets per day. It is a very difficult and challenging task to analyze such huge data. The querying and retrieval of data is also difficult for this situation. Such millions of tweets contain maximum amount of noise and redundancy. The searching in such raw tweets is a very complicated task. The solution is just filtering such tweets for important contents but this is also difficult for searching through such huge tweets because of noise and redundancy. So the possible solution to information overload problem is summarization. Summarization represents restating of the main ideas of the text in as few words as possible. There are various algorithms are available for tweet summarization, some of them focus on static and small-scale data set and others on dynamic, fast arriving, and large-scale tweet streams. Here, we make survey of various approaches for tweet summarization.

Keywords: Tweet stream, continuous summarization, tweet clustering, summary, timeline.