Shortlisting is the task of reducing a long list of alternatives to a
(smaller) set of best or most suitable alternatives from which a final winner
will be chosen. Shortlisting is often used in the nomination process of awards
or in recommender systems to display featured objects. In this paper, we
analyze shortlisting methods that are based on approval data, a common type of
preferences. Furthermore, we assume that the size of the shortlist, i.e., the
number of best or most suitable alternatives, is not fixed but determined by
the shortlisting method. We axiomatically analyze established and new
shortlisting methods and complement this analysis with an experimental
evaluation based on biased voters and noisy quality estimates. Our results lead
to recommendations which shortlisting methods to use, depending on the desired
properties