  The ability to fuse sentences is highly attractive for summarization systems
because it is an essential step to produce succinct abstracts. However, to
date, summarizers can fail on fusing sentences. They tend to produce few
summary sentences by fusion or generate incorrect fusions that lead the summary
to fail to retain the original meaning. In this paper, we explore the ability
of Transformers to fuse sentences and propose novel algorithms to enhance their
ability to perform sentence fusion by leveraging the knowledge of points of
correspondence between sentences. Through extensive experiments, we investigate
the effects of different design choices on Transformer's performance. Our
findings highlight the importance of modeling points of correspondence between
sentences for effective sentence fusion.
