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Background  This article provides an overview of the first  BioASQ  challenge, a competition on large-scale biomedical semantic indexing and question answering ( QA ), which took place between March and September 2013.  BioASQ  assesses the ability of systems to semantically index very large numbers of biomedical scientific articles, and to return concise and user-understandable answers to given natural language questions by combining information from biomedical articles and ontologies.    Results  The 2013  BioASQ  competition comprised two tasks, Task 1a and Task 1b. In Task 1a participants were asked to automatically annotate new  PubMed  documents with  MeSH  headings. Twelve teams participated in Task 1a, with a total of 46 system runs submitted, and one of the teams performing consistently better than the  MTI  indexer used by  NLM  to suggest  MeSH  headings to curators. Task 1b used benchmark datasets containing 29 development and 282 test English questions, along with gold standard (reference) answers, prepared by a team of biomedical experts from around Europe and participants had to automatically produce answers. Three teams participated in Task 1b, with 11 system runs. The  BioASQ  infrastructure, including benchmark datasets, evaluation mechanisms, and the results of the participants and baseline methods, is publicly available.    Conclusions  A publicly available evaluation infrastructure for biomedical semantic indexing and  QA  has been developed, which includes benchmark datasets, and can be used to evaluate systems that: assign  MeSH  headings to published articles or to English questions; retrieve relevant  RDF  triples from ontologies, relevant articles and snippets from  PubMed  Central; produce “exact” and paragraph-sized “ideal” answers (summaries). The results of the systems that participated in the 2013  BioASQ  competition are promising. In Task 1a one of the systems performed consistently better from the  NLM ’s  MTI  indexer. In Task 1b the systems received high scores in the manual evaluation of the “ideal” answers; hence, they produced high quality summaries as answers. Overall,  BioASQ  helped obtain a unified view of how techniques from text classification, semantic indexing, document and passage retrieval, question answering, and text summarization can be combined to allow biomedical experts to obtain concise, user-understandable answers to questions reflecting their real information needs.    Electronic supplementary material  The online version of this article (doi:10.1186/s12859-015-0564-6) contains supplementary material, which is available to authorized users.

An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition