Tell Me How to Survey: Literature Review Made Simple with Automatic Reading Path Generation

Ding, Jiayuan; Xiang, Tong; Ou, Zijing; Zuo, Wangyang; Zhao, Ruihui; Lin, Chenghua; Zheng, Yefeng; Liu, Bang

doi:10.1109/icde53745.2022.00322

Cited by 2 publications

(1 citation statement)

References 40 publications

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“…Although semantic MPA proved to improve ranking pertinence by a large margin, there seemed to still large space to improve recall. To achieve this, we guess that it may be helpful to start and guide main path exploration by first ranking and selecting important publications in some way (Bae et al, 2014; Zhang et al, 2014; Tao et al, 2017; Ding et al, 2022).…”

Section: Quantitative Analysismentioning

confidence: 99%

Extracting the evolutionary backbone of scientific domains: The semantic main path network analysis approach based on citation context analysis

Jiang

Liu

2023

Asso for Info Science & Tech

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Main path analysis is a popular method for extracting the scientific backbone from the citation network of a research domain. Existing approaches ignored the semantic relationships between the citing and cited publications, resulting in several adverse issues, in terms of coherence of main paths and coverage of significant studies. This paper advocated the semantic main path network analysis approach to alleviate these issues based on citation function analysis.A wide variety of SciBERT-based deep learning models were designed for identifying citation functions. Semantic citation networks were built by either including important citations, for example, extension, motivation, usage and similarity, or excluding incidental citations like background and future work.Semantic main path network was built by merging the top-K main paths extracted from various time slices of semantic citation network. In addition, a three-way framework was proposed for the quantitative evaluation of main path analysis results. Both qualitative and quantitative analysis on three research areas of computational linguistics demonstrated that, compared to semantics-agnostic counterparts, different types of semantic main path networks provide complementary views of scientific knowledge flows. Combining them together, we obtained a more precise and comprehensive picture of domain evolution and uncover more coherent development pathways between scientific ideas.

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Section: Quantitative Analysismentioning

confidence: 99%

Extracting the evolutionary backbone of scientific domains: The semantic main path network analysis approach based on citation context analysis

Jiang

Liu

2023

Asso for Info Science & Tech

View full text Add to dashboard Cite

show abstract

Heterogeneous hypergraph learning for literature retrieval based on citation intents

Shi,

Liu,

2024

Scientometrics

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Tell Me How to Survey: Literature Review Made Simple with Automatic Reading Path Generation

Abstract: This is a repository copy of Tell me how to survey: literature review made simple with automatic reading path generation.

Cited by 2 publications

References 40 publications

Extracting the evolutionary backbone of scientific domains: The semantic main path network analysis approach based on citation context analysis

Extracting the evolutionary backbone of scientific domains: The semantic main path network analysis approach based on citation context analysis

Heterogeneous hypergraph learning for literature retrieval based on citation intents

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