Capturing Relations between Scientific Papers: An Abstractive Model for Related Work Section Generation
Xiuying Chen, Hind Alamro, Mingzhe Li, Shen Gao, Xiangliang Zhang, Dongyan Zhao, Rui Yan
Abstract
Given a set of related publications, related work section generation aims to provide researchers with an overview of the specific research area by summarizing these works and introducing them in a logical order. Most of existing related work generation models follow the inflexible extractive style, which directly extract sentences from multiple original papers to form a related work discussion. Hence, in this paper, we propose a Relation-aware Related work Generator (RRG), which generates an abstractive related work from the given multiple scientific papers in the same research area. Concretely, we propose a relation-aware multi-document encoder that relates one document to another according to their content dependency in a relation graph. The relation graph and the document representation are interacted and polished iteratively, complementing each other in the training process. We also contribute two public datasets composed of related work sections and their corresponding papers. Extensive experiments on the two datasets show that the proposed model brings substantial improvements over several strong baselines. We hope that this work will promote advances in related work generation task.
BibTeX
@inproceedings{chen-etal-2021-capturing,
title = "Capturing Relations between Scientific Papers: An Abstractive Model for Related Work Section Generation",
author = "Chen, Xiuying and
Alamro, Hind and
Li, Mingzhe and
Gao, Shen and
Zhang, Xiangliang and
Zhao, Dongyan and
Yan, Rui",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.473/",
doi = "10.18653/v1/2021.acl-long.473",
pages = "6068--6077"
}