  Automatic text summarization tools have a great impact on many fields, such
as medicine, law, and scientific research in general. As information overload
increases, automatic summaries allow handling the growing volume of documents,
usually by assigning weights to the extracted phrases based on their
significance in the expected summary. Obtaining the main contents of any given
document in less time than it would take to do that manually is still an issue
of interest. In~this~ article, a new method is presented that allows
automatically generating extractive summaries from documents by adequately
weighting sentence scoring features using \textit{Particle Swarm Optimization}.
The key feature of the proposed method is the identification of those features
that are closest to the criterion used by the individual when summarizing. The
proposed method combines a binary representation and a continuous one, using an
original variation of the technique developed by the authors of this paper. Our
paper shows that using user labeled information in the training set helps to
find better metrics and weights. The empirical results yield an improved
accuracy compared to previous methods used in this field
