Twitter Based Event Summarization

Twitter, a Social networking service produce a huge quantity of data daily for many trending real-world events. As hundreds of millions of Twitter users generate many posts on a daily basis, therefore it’s very challenging to extract and summarize the user-generated content. Moreover, the Twitter API also provides only latest posts in a sequential order. This motivates the dire need for a new automatic event summarization system that provides the informative summaries of user-generated content that might help in making decisions supporting intelligence. In this paper, we intend to summarize the twitter posts corresponding to twitter hashtags to find a representative post among a set of posts that correspond to the same hashtag, with the intent to identify the strongly relevant post. We used two approaches Temporal TF-IDF and Temporal TF-IDF with keyword importance for finding the summary of the events. Then we evaluate and compare these approaches using a self-generated dataset of Twitter posts and show that our system automatically select posts that are more relevant.