This paper presents a new algorithm for automatic summarization of specialized texts combining terminological and semantic resources: a term extractor and an ontology. The term extractor provides the list of the terms that are present in the text together their corresponding termhood. The ontology is used to calculate the semantic similarity among the terms found in the main body and those present in the document title. The general idea is to obtain a relevance score for each sentence taking into account both the termhood of the terms found in such sentence and the similarity among such terms and those terms present in the title of the document. The phrases with the highest score are chosen to take part of the final summary. We evaluate the algorithm with Rouge, comparing the resulting summaries with the summaries of other summarizers. The sentence selection algorithm was also tested as part of a standalone summarizer. In both cases it obtains quite good results although the perception is that there is a space for improvement.