Capturing Relations between Scientific Papers: An Abstractive Model for Related Work Section Generation

Chen, Xiuying; Alamro, Hind; Li, Mingzhe; Gao, Shen; Zhang, Xiangliang; Zhao, Dongyan; Yan, Rui

doi:10.18653/v1/2021.acl-long.473

Cited by 17 publications

(43 citation statements)

References 21 publications

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“…In a parallel line, Chen et al [18] work on the problem of generating related work section paragraphs with two or more citations given multiple cited papers' abstracts. They derive their related work generation dataset from S2ORC [73] and Delve [4], both of which are large datasets with a large connected citation graph.…”

Section: Capturing Relations Between Scientific Papersmentioning

confidence: 99%

“…Chen et al [18] is the first work on generating related work paragraphs with multiple citations given multiple cited papers' abstracts. Their task setting is closer to the real related work section generation scenario.…”

Section: Capturing Relations Between Scientific Papersmentioning

confidence: 99%

“…In parallel to Ge et al [37] and Chen et al [18]: Luu et al [75] frame their task as using citation sentence as partial supervision for explaining relationships between two scientific documents. They use citation sentences in the S2ORC dataset [73] with a single reference that links back to the corpus as their dataset.…”

Section: Explaining Relationships Between Scientific Documentsmentioning

confidence: 99%

“…Inputs Target AbuRa'ed et al [2] Cited title, abstract Citation sentence w/ single reference Xing et al [117] Context sentences, Citation sentence w/ single single cited abstract reference Ge et al [37] Citation network, single cited Citation sentence, citation abstract, context sentences function, salient sentence in cited abstracts Luu et al [75] Intro of the citing paper, Citation sentence w/ single reference named entities of the cited papers Chen et al [18] Cited abstracts A paragraph w/ 2+ citations As a pioneering work abstractive citation sentence generation, AbuRa'ed et al [2] work on the abstractive generation of citation sentences given the concatenation of cited paper's title and abstract. In parallel, Xing et al [117]'s model takes 3 sentences each before and after the target citation sentence, and the cited paper's abstract as the inputs.…”

Section: Prior Workmentioning

confidence: 99%

“…Although the problem of automatic related work generation was proposed over 10 years ago, this problem received little attention until very recently, when a few works revisited this problem with neural network-based systems [2,18,25,37,75,117]. There are only a few existing papers that explicitly study related work generation.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Automatic Related Work Generation: A Meta Study

Li¹,

Ouyang²

2022

Preprint

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Academic research is an exploration activity to solve problems that have never been resolved before. By this nature, each academic research work is required to perform a literature review to distinguish its novelties that have not been addressed by prior works. In natural language processing, this literature review is usually conducted under the "Related Work" section. The task of automatic related work generation aims to automatically generate the "Related Work" section given the rest of the research paper and a list of cited papers. Although this task was proposed over 10 years ago, it received little attention until very recently, when it was cast as a variant of the scientific multi-document summarization problem. However, even till today, the problems of automatic related work and citation text generation are not yet standardized. In this survey, we conduct a meta-study to compare the existing literature on related work generation from the perspectives of problem formulation, dataset collection, methodological approach, performance evaluation, and future prospects to provide the reader insight into the progress of the state-of-the-art studies, as well as and how future studies can be conducted. We also survey relevant fields of study that we suggest future work to consider integrating.

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Section: Capturing Relations Between Scientific Papersmentioning

confidence: 99%

Section: Capturing Relations Between Scientific Papersmentioning

confidence: 99%

Section: Explaining Relationships Between Scientific Documentsmentioning

confidence: 99%

Section: Prior Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Automatic Related Work Generation: A Meta Study

Li¹,

Ouyang²

2022

Preprint

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Reinforced Subject-Aware Graph Neural Network for Related Work Generation

Yu,

Zhang,

Shi

et al. 2024

Knowledge Science, Engineering and Management

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Medicine-Engineering Interdisciplinary Research Based on Bibliometric Analysis: A Case Study on Medicine-Engineering Institutional Cooperation of Shanghai Jiao Tong University

Wang

Cui

Deng

2022

J. Shanghai Jiaotong Univ. (Sci.)

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This article aims to provide reference for medicine-engineering interdisciplinary research. Targeted at the scientific literature and patent literature published by Shanghai Jiao Tong University, this article attempts to set up co-occurrence matrix of medicine-engineering institutional information which was extracted from address fields of the papers, so as to construct the medicine-engineering intersection datasets. The dataset of scientific literature was analyzed using bibliometrics and visualization methods from multiple dimensions, and the most active factors, such as trends of output, journal and subject distribution, were identified from the indicators of category normalized citation impact (CNCI), times cited, keywords, citation topics and the degree of medicine-engineering interdisplinary. Research on hotspots and trends was discussed in detail. Analyses of the dataset of patent literature showed research themes and measured the degree for technology convergence of medicine-engineering.

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Capturing Relations between Scientific Papers: An Abstractive Model for Related Work Section Generation

Cited by 17 publications

References 21 publications

Automatic Related Work Generation: A Meta Study

Automatic Related Work Generation: A Meta Study

Reinforced Subject-Aware Graph Neural Network for Related Work Generation

Medicine-Engineering Interdisciplinary Research Based on Bibliometric Analysis: A Case Study on Medicine-Engineering Institutional Cooperation of Shanghai Jiao Tong University

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