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Leveraging full-text article exploration for citation analysis

Author

Listed:
  • Moreno La Quatra

    (Politecnico di Torino Corso Duca degli Abruzzi, 24)

  • Luca Cagliero

    (Politecnico di Torino Corso Duca degli Abruzzi, 24)

  • Elena Baralis

    (Politecnico di Torino Corso Duca degli Abruzzi, 24)

Abstract

Scientific articles often include in-text citations quoting from external sources. When the cited source is an article, the citation context can be analyzed by exploring the article full-text. To quickly access the key information, researchers are often interested in identifying the sections of the cited article that are most pertinent to the text surrounding the citation in the citing article. This paper first performs a data-driven analysis of the correlation between the textual content of the sections of the cited article and the text snippet where the citation is placed. The results of the correlation analysis show that the title and abstract of the cited article are likely to include content highly similar to the citing snippet. However, the subsequent sections of the paper often include cited text snippets as well. Hence, there is a need to understand the extent to which an exploration of the full-text of the cited article would be beneficial to gain insights into the citing snippet, considering also the fact that the full-text access could be restricted. To this end, we then propose a classification approach to automatically predicting whether the cited snippets in the full-text of the paper contain a significant amount of new content beyond abstract and title. The proposed approach could support researchers in leveraging full-text article exploration for citation analysis. The experiments conducted on real scientific articles show promising results: the classifier has a 90% chance to correctly distinguish between the full-text exploration and only title and abstract cases.

Suggested Citation

  • Moreno La Quatra & Luca Cagliero & Elena Baralis, 2021. "Leveraging full-text article exploration for citation analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(10), pages 8275-8293, October.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:10:d:10.1007_s11192-021-04117-4
    DOI: 10.1007/s11192-021-04117-4
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    References listed on IDEAS

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    1. Chanwoo Jeong & Sion Jang & Eunjeong Park & Sungchul Choi, 2020. "A context-aware citation recommendation model with BERT and graph convolutional networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 1907-1922, September.
    2. Chrysoula Zerva & Minh-Quoc Nghiem & Nhung T. H. Nguyen & Sophia Ananiadou, 2020. "Cited text span identification for scientific summarisation using pre-trained encoders," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 3109-3137, December.
    3. Moreno La Quatra & Luca Cagliero & Elena Baralis, 2020. "Exploiting pivot words to classify and summarize discourse facets of scientific papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 3139-3157, December.
    4. Shutian Ma & Jin Xu & Chengzhi Zhang, 2018. "Automatic identification of cited text spans: a multi-classifier approach over imbalanced dataset," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 1303-1330, August.
    5. Tarek Saier & Michael Färber, 2020. "unarXive: a large scholarly data set with publications’ full-text, annotated in-text citations, and links to metadata," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 3085-3108, December.
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