  Communicating new research ideas involves highlighting similarities and
differences with past work. Authors write fluent, often long sections to survey
the distinction of a new paper with related work. In this work we model
generating related work sections while being cognisant of the motivation behind
citing papers. Our content planning model generates a tree of cited papers
before a surface realization model lexicalizes this skeleton. Our model
outperforms several strong state-of-the-art summarization and multi-document
summarization models on generating related work on an ACL Anthology (AA) based
dataset which we contribute.
