  Multi-document summarization (MDS) is a challenging task, often decomposed to
subtasks of salience and redundancy detection, followed by text generation. In
this context, alignment of corresponding sentences between a reference summary
and its source documents has been leveraged to generate training data for some
of the component tasks. Yet, this enabling alignment step has usually been
applied heuristically on the sentence level on a limited number of subtasks. In
this paper, we propose extending the summary-source alignment framework by (1)
applying it at the more fine-grained proposition span level, (2) annotating
alignment manually in a multi-document setup, and (3) revealing the great
potential of summary-source alignments to yield several datasets for at least
six different tasks. Specifically, for each of the tasks, we release a manually
annotated test set that was derived automatically from the alignment
annotation. We also release development and train sets in the same way, but
from automatically derived alignments. Using the datasets, each task is
demonstrated with baseline models and corresponding evaluation metrics to spur
future research on this broad challenge.
