The minutes of the meetings of the Swedish Central Bank are analyzed from the other banks manually. In finance the reaction of each event as to be as quick as possible, so the goal is to automate the text comprehension process using machine learning algorithms. The problem can be divided into two main tasks, summarize the speech of each board member and find his sentiment. To retrieve the summary has been used an unsupervised approach based on the Text Rank and the Latent Semantic Analysis algorithms, combined with information about the most discussed topics in this kind of meeting. The sentiment behind this kind of economical text cannot be identified as positive or negative, as in most of the literature, but it can be classified as hawkish or dovish.