In the context of Learning Technologies, the need to be able to assess the learning and domain comprehension in open-ended learner responses has been present in artificial intelligence and education since its beginnings. The advantage of using summaries is that they allow teachers to diagnose comprehension and the amount of information remembered from text in the learning process. This study addresses the issue of automatically obtaining overall discourse scores from surface discourse measures for Basque language. Global measures have been studied for cohesion, adequacy and use of language. The approach taken was to estimate the presence of the automatically gathered surface discourse measures in expert grading decisions in cohesion, adequacy and use of language. As a consequence, three grading decision-making regression models were obtained to estimate overall grades from text written in Basque. Next, the obtained regression models were tested in corpus-containing summaries written by learners with different degrees of summarisation maturity. The results show that the obtained grading frameworks significantly reflect human decisions and are able to discriminate summarisation maturity differences.