Tweet contextualization: combining sentence extraction, sentence aggregation and sentence reordering to enhance informativeness and readability

Abstract

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 280 characters long message that is rarely self-content. Hence, additional information is 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 paragraph, called a context, from a set of documents in response to topics treated by the tweet. Furthermore of being informative, a summary should be coherent, i.e., well-written to be readable and grammatically compact. Hence, coherence is an essential characteristic in order to produce comprehensible texts. In this paper, we propose a new approach of tweet contextualization based on graphs by combining sentence extraction, sentence aggregation and sentence reordering to enhance informativeness and readability in order to build a relevant and coherent context. The main idea of our proposed method is to select relevant, informative coherent and semantically related sentences from a document that best describes themes expressed by the tweet, and aggregate relevant phrases in the same graph to filter more informative ones. We proposed a novel algorithm called CSA algorithm to achieve our aim and to construct a concise extract. We also proposed to invest in a reordering phase to improve the coherence of the obtained context.