  Sequence-to-sequence (s2s) models are the basis for extensive work in natural
language processing. However, some applications, such as multi-document
summarization, multi-modal machine translation, and the automatic post-editing
of machine translation, require mapping a set of multiple distinct inputs into
a single output sequence. Recent work has introduced bespoke architectures for
these multi-input settings, and developed models which can handle increasingly
longer inputs; however, the performance of special model architectures is
limited by the available in-domain training data. In this work we propose a
simple decoding methodology which ensembles the output of multiple instances of
the same model on different inputs. Our proposed approach allows models trained
for vanilla s2s tasks to be directly used in multi-input settings. This works
particularly well when each of the inputs has significant overlap with the
others, as when compressing a cluster of news articles about the same event
into a single coherent summary, and we obtain state-of-the-art results on
several multi-document summarization datasets.
