Most existing reading comprehension datasets focus on single-span answers, which can be extracted as a single contiguous span from a given text passage. Multi-span questions, i.e., questions whose answer is a series of multiple discontiguous spans in the text, are common in real life but are less studied. In this paper, we present MultiSpanQA, a new dataset that focuses on questions with multi-span answers. Raw questions and contexts are extracted from the Natural Questions (Kwiatkowski et al., 2019) dataset. After multi-span re-annotation, MultiSpanQA consists of over a total of 6,000 multi-span questions in the basic version, and over 19,000 examples with unanswerable questions, and questions with single-, and multi-span answers in the expanded version. We introduce new metrics for the purposes of multi-span question answering evaluation, and establish several baselines using advanced models. Finally, we propose a new model which beats all baselines and achieves the state-of-the-art on our dataset. © 2022 Association for Computational Linguistics.