Automatic text summarization methods are increasingly needed nowadays. Extractive multi-document
summarization approaches aim to obtain the main content of a document collection at the same time
that the redundant information is reduced. This can be addressed from an optimization point of view.
There is a lack of multi-objective approaches applied in this context. In this paper, a Multi-Objective Arti-
ficial Bee Colony (MOABC) algorithm has been designed and implemented for this task. Experiments have
been performed based on datasets from Document Understanding Conference (DUC) and model perfor-
mances have been evaluated with Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics, as
is usual in this knowledge field. The results of the proposed approach show important improvements,
i.e., in average, 31.09% (8.43%) and 18.63% (6.09%) of improvement in ROUGE-2 (ROUGE-L) have been
obtained with respect to the best single-objective and multi-objective results in the scientific literature.
Even more, the proposed approach has been proven to produce more concentrated ROUGE values when
the algorithm execution is repeated (between 620.63% and 1333.95% of reduction in the relative disper-
sion, that is, between 6 and 13 times better), leading to more robust results.