Extracting Keywords on SNS by Successive KeyGraph

Social networking services (SNS) are important sources of news for the world. By summarizing text on SNS, we will be able to grasp what topics in the news are going on. However, because texts keep growing in SNS, we need to process huge amounts of data very efficiently. Therefore, it is necessary to apply a fast and low-memory algorithm. In this investigation, we propose Successive KeyGraph as a method of keywords extraction. The extracted keywords work effectively for news summarization. In Successive KeyGraph, we update keygraph over time. KeyGraph extracts the author's claims and outputs the results immediately. Also, we hold co-occurrence count which we need to make a keygraph current by using Lossy Counting. We apply several preprocessing to manage unknown words and to improve morphological analysis.