Generating Related Work

• URL: http://arxiv.org/abs/2104.08668v1
• Date: Sun, 18 Apr 2021 00:19:37 GMT
• Title: Generating Related Work
• Authors: Darsh J Shah and Regina Barzilay
• Abstract summary: We model generating related work sections while being cognisant of the motivation behind citing papers. Our model outperforms several strong state-of-the-art summarization and multi-document summarization models.
• Score: 37.161925758727456
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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