  We investigate the problem of reader-aware multi-document summarization
(RA-MDS) and introduce a new dataset for this problem. To tackle RA-MDS, we
extend a variational auto-encodes (VAEs) based MDS framework by jointly
considering news documents and reader comments. To conduct evaluation for
summarization performance, we prepare a new dataset. We describe the methods
for data collection, aspect annotation, and summary writing as well as
scrutinizing by experts. Experimental results show that reader comments can
improve the summarization performance, which also demonstrates the usefulness
of the proposed dataset. The annotated dataset for RA-MDS is available online.
