A tweet summarization method based on a keyword graph

There are a huge number of posts on the micro blogs such as Twitter and thus it can be an important information source of various domains. However, the information density of each post, tweet, is too low because the length of tweets is too short. Due to the huge amount and low information density, it is hard to obtain useful information from Twitter such as the public opinion trend. Considering these characteristics of tweets, we propose a novel tweet summarization method. The proposed method first finds the strongly related groups of words based on keyword graphs. In the graphs, the frequent words are the vertexes and the co-occurrences are the edges. We use the maximum κ-clique method to find strongly related groups of words, and summarize the tweets which include the words in groups. We confirmed the proposed method is effective for summarizing of tweets and is superior to the existing method with the experiments.