Word embeddings are nowadays widely deployed in a large number of Natural Lan-guage Processing tasks. A word embedding is used to map each word belonging toa corpus, into a vector space, keeping semantic and syntactic properties. They areused in different implementations such as sentiment analysis, topic extraction, Part-Of-Speech tagging and of course document summarization. The focus of this thesisis towards this last job: the object is to extrapolate, given a collection of articles, themost relevant sentences to provide the reader only a limited set of information buthopefully the most meaningful.Particularly, the scope of this work is to empirically show that a domain-specific wordembedding is able to extract a better summary with respect to a general one.