Leveraging Hybrid Citation Context for Impact Summarization

Measuring the citation context of national self‐references

Journal of the Association for Information Science and Technology ◽

10.1002/asi.24569 ◽

2021 ◽

Author(s):

Liyue Chen ◽

Jielan Ding ◽

Vincent Larivière

Keyword(s):

Citation Context

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Citation context-based topic models: discovering cited and citing topics from full text

Library Hi Tech ◽

10.1108/lht-01-2021-0041 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Lixue Zou ◽

Xiwen Liu ◽

Wray Buntine ◽

Yanli Liu

Keyword(s):

Full Text ◽

Topic Models ◽

Content Type ◽

Reference Information ◽

Citation Context ◽

Comparable Performance ◽

Automatic Text Analysis ◽

Collapsed Gibbs Sampling ◽

Source Of Information ◽

Automatic Text

PurposeFull text of a document is a rich source of information that can be used to provide meaningful topics. The purpose of this paper is to demonstrate how to use citation context (CC) in the full text to identify the cited topics and citing topics efficiently and effectively by employing automatic text analysis algorithms.Design/methodology/approachThe authors present two novel topic models, Citation-Context-LDA (CC-LDA) and Citation-Context-Reference-LDA (CCRef-LDA). CC is leveraged to extract the citing text from the full text, which makes it possible to discover topics with accuracy. CC-LDA incorporates CC, citing text, and their latent relationship, while CCRef-LDA incorporates CC, citing text, their latent relationship and reference information in CC. Collapsed Gibbs sampling is used to achieve an approximate estimation. The capacity of CC-LDA to simultaneously learn cited topics and citing topics together with their links is investigated. Moreover, a topic influence measure method based on CC-LDA is proposed and applied to create links between the two-level topics. In addition, the capacity of CCRef-LDA to discover topic influential references is also investigated.FindingsThe results indicate CC-LDA and CCRef-LDA achieve improved or comparable performance in terms of both perplexity and symmetric Kullback–Leibler (sKL) divergence. Moreover, CC-LDA is effective in discovering the cited topics and citing topics with topic influence, and CCRef-LDA is able to find the cited topic influential references.Originality/valueThe automatic method provides novel knowledge for cited topics and citing topics discovery. Topic influence learnt by our model can link two-level topics and create a semantic topic network. The method can also use topic specificity as a feature to rank references.

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CO‐CITATION CONTEXT ANALYSIS AND THE STRUCTURE OF PARADIGMS

Journal of Documentation ◽

10.1108/eb026695 ◽

1980 ◽

Vol 36 (3) ◽

pp. 183-196 ◽

Cited By ~ 99

Author(s):

HENRY SMALL

Keyword(s):

Context Analysis ◽

Citation Context

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A novel machine-learning approach to measuring scientific knowledge flows using citation context analysis

Scientometrics ◽

10.1007/s11192-018-2767-x ◽

2018 ◽

Vol 116 (2) ◽

pp. 973-996 ◽

Cited By ~ 19

Author(s):

Saeed-Ul Hassan ◽

Iqra Safder ◽

Anam Akram ◽

Faisal Kamiran

Keyword(s):

Machine Learning ◽

Scientific Knowledge ◽

Knowledge Flows ◽

Learning Approach ◽

Context Analysis ◽

Citation Context ◽

Machine Learning Approach

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ANALYSIS OF REFERENCE RELATIONSHIPS AMONG RESEARCH PAPERS, BASED ON CITATION CONTEXT

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213012400040 ◽

2012 ◽

Vol 21 (02) ◽

pp. 1240004 ◽

Cited By ~ 4

Author(s):

WENJUAN WANG ◽

PAUL VILLAVICENCIO ◽

TOYOHIDE WATANABE

Keyword(s):

Experimental Data ◽

Data Evaluation ◽

Research Papers ◽

Relationship Extraction ◽

Citation Context ◽

Scientific Papers ◽

Approach Method ◽

Paper Abstract

Many efforts have been successfully paid by researchers or developers to grasp efficiently the contents of related works with a view to making their investigations successfully, preparing their plans effectively or attaining their objectives smartly. Although the paper abstract prepared by authors themselves is one of the useful efforts in many cases the content is not always sufficient by them to know the features of objective, approach, method, experimental data, evaluation, etc. in comparison with other related works. In this paper, we focus on the text associated with citation to reveal reference relationships between papers in order to achieve an objective of analyzing the influence of related works. Citation indicates the connection between two papers. Also, the text associated with citation can reflect the contribution of scientific papers, expressions of authors' opinions or other researches and also can show the usage of the research to resolve problems. Our main discussion points in this paper are classification of reference relationship, extraction of text associated with citation and representation of reference relationship from a viewpoint of making the original features of our work clear.

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