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    <abstract>In a citation graph, adjacent paper nodes share related scientific terms and topics. The graph thus conveys unique structure information of document-level relatedness that can be utilized in the paper summarization task, for exploring beyond the intra-document information.In this work, we focus on leveraging citation graphs to improve scientific paper extractive summarization under different settings.We first propose a Multi-granularity Unsupervised Summarization model (MUS) as a simple and low-cost solution to the task.MUS finetunes a pre-trained encoder model on the citation graph by link prediction tasks.Then, the abstract sentences are extracted from the corresponding paper considering multi-granularity information.Preliminary results demonstrate that citation graph is helpful even in a simple unsupervised framework.Motivated by this, we next propose a Graph-based Supervised Summarizationmodel (GSS) to achieve more accurate results on the task when large-scale labeled data are available.Apart from employing the link prediction as an auxiliary task, GSS introduces a gated sentence encoder and a graph information fusion module to take advantage of the graph information to polish the sentence representation.Experiments on a public benchmark dataset show that MUS and GSS bring substantial improvements over the prior state-of-the-art model.</abstract>
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%0 Conference Proceedings
%T Scientific Paper Extractive Summarization Enhanced by Citation Graphs
%A Chen, Xiuying
%A Li, Mingzhe
%A Gao, Shen
%A Yan, Rui
%A Gao, Xin
%A Zhang, Xiangliang
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F chen-etal-2022-scientific
%X In a citation graph, adjacent paper nodes share related scientific terms and topics. The graph thus conveys unique structure information of document-level relatedness that can be utilized in the paper summarization task, for exploring beyond the intra-document information.In this work, we focus on leveraging citation graphs to improve scientific paper extractive summarization under different settings.We first propose a Multi-granularity Unsupervised Summarization model (MUS) as a simple and low-cost solution to the task.MUS finetunes a pre-trained encoder model on the citation graph by link prediction tasks.Then, the abstract sentences are extracted from the corresponding paper considering multi-granularity information.Preliminary results demonstrate that citation graph is helpful even in a simple unsupervised framework.Motivated by this, we next propose a Graph-based Supervised Summarizationmodel (GSS) to achieve more accurate results on the task when large-scale labeled data are available.Apart from employing the link prediction as an auxiliary task, GSS introduces a gated sentence encoder and a graph information fusion module to take advantage of the graph information to polish the sentence representation.Experiments on a public benchmark dataset show that MUS and GSS bring substantial improvements over the prior state-of-the-art model.
%R 10.18653/v1/2022.emnlp-main.270
%U https://aclanthology.org/2022.emnlp-main.270/
%U https://doi.org/10.18653/v1/2022.emnlp-main.270
%P 4053-4062

Markdown (Informal)

[Scientific Paper Extractive Summarization Enhanced by Citation Graphs](https://aclanthology.org/2022.emnlp-main.270/) (Chen et al., EMNLP 2022)

• Scientific Paper Extractive Summarization Enhanced by Citation Graphs (Chen et al., EMNLP 2022)

ACL

• Xiuying Chen, Mingzhe Li, Shen Gao, Rui Yan, Xin Gao, and Xiangliang Zhang. 2022. Scientific Paper Extractive Summarization Enhanced by Citation Graphs. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 4053–4062, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.