Generating Related Work
Computation and Language
2021-04-20 v1
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.
Cite
@article{arxiv.2104.08668,
title = {Generating Related Work},
author = {Darsh J Shah and Regina Barzilay},
journal= {arXiv preprint arXiv:2104.08668},
year = {2021}
}