  The related work section is an important component of a scientific paper,
which highlights the contribution of the target paper in the context of the
reference papers. Authors can save their time and effort by using the
automatically generated related work section as a draft to complete the final
related work. Most of the existing related work section generation methods rely
on extracting off-the-shelf sentences to make a comparative discussion about
the target work and the reference papers. However, such sentences need to be
written in advance and are hard to obtain in practice. Hence, in this paper, we
propose an abstractive target-aware related work generator (TAG), which can
generate related work sections consisting of new sentences. Concretely, we
first propose a target-aware graph encoder, which models the relationships
between reference papers and the target paper with target-centered attention
mechanisms. In the decoding process, we propose a hierarchical decoder that
attends to the nodes of different levels in the graph with keyphrases as
semantic indicators. Finally, to generate a more informative related work, we
propose multi-level contrastive optimization objectives, which aim to maximize
the mutual information between the generated related work with the references
and minimize that with non-references. Extensive experiments on two public
scholar datasets show that the proposed model brings substantial improvements
over several strong baselines in terms of automatic and tailored human
evaluations.
