Cliques detection vs maximum spanning tree for tweet contextualization

Nowadays, social medias are very popular among their users. One of the most well-known social networks is Twitter. It is a micro-blog that enables its users to send short messages called tweets. A tweet is a 140 characters long message that is rarely self-cont, hence additional information are necessary to allow better readability of the tweet. This new task has attracted a great deal of attention recently. Given a tweet, the aim of tweet contextualization is to produce an informative and coherent paragraph, called a context, from a set of documents in response to topics treated by the tweet. In this paper, we propose a new approach of Tweet Contextualization based on combining automatic summarization techniques and sentence aggregation. The main idea of our proposed method is to select relevant, informative and semantically related sentences that best describe themes expressed by the tweet, and then build a concise context.