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.
翻译：交流新的研究想法涉及突出与过去工作的相似之处和不同之处。作者编写流利的、往往长篇的章节来调查新论文与相关工作的区别。在这项工作中,我们模拟产生相关的工作部分,同时认识到引文背后的动机。我们的内容规划模型在地表化模型将这一骨骼化之前产生一棵被引用的文件。我们的模型优于若干强效的最新总结和多份文件总结模型,这些模型涉及我们所贡献的、基于ACLAnthlogic(AAA)的数据集的相关工作。
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