  We study the task of predicting a set of salient questions from a given
paragraph without any prior knowledge of the precise answer. We make two main
contributions. First, we propose a new method to evaluate a set of predicted
questions against the set of references by using the Hungarian algorithm to
assign predicted questions to references before scoring the assigned pairs. We
show that our proposed evaluation strategy has better theoretical and practical
properties compared to prior methods because it can properly account for the
coverage of references. Second, we compare different strategies to utilize a
pre-trained seq2seq model to generate and select a set of questions related to
a given paragraph. The code is available.
