  Multi-document summarization (MDS) assumes a set of topic-related documents
are provided as input. In practice, this document set is not always available;
it would need to be retrieved given an information need, i.e. a question or
topic statement, a setting we dub "open-domain" MDS. We study this more
challenging setting by formalizing the task and bootstrapping it using existing
datasets, retrievers and summarizers. Via extensive automatic and human
evaluation, we determine: (1) state-of-the-art summarizers suffer large
reductions in performance when applied to open-domain MDS, (2) additional
training in the open-domain setting can reduce this sensitivity to imperfect
retrieval, and (3) summarizers are insensitive to the retrieval of duplicate
documents and the order of retrieved documents, but highly sensitive to other
errors, like the retrieval of irrelevant documents. Based on our results, we
provide practical guidelines to enable future work on open-domain MDS, e.g. how
to choose the number of retrieved documents to summarize. Our results suggest
that new retrieval and summarization methods and annotated resources for
training and evaluation are necessary for further progress in the open-domain
setting.
