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Information extraction from scientific articles: a survey

Author

Listed:
  • Zara Nasar

    (University of the Punjab)

  • Syed Waqar Jaffry

    (University of the Punjab)

  • Muhammad Kamran Malik

    (University of the Punjab)

Abstract

In last few decades, with the advent of World Wide Web (WWW), world is being overloaded with huge data. This huge data carries potential information that once extracted, can be used for betterment of humanity. Information from this data can be extracted using manual and automatic analysis. Manual analysis is not scalable and efficient, whereas, the automatic analysis involves computing mechanisms that aid in automatic information extraction over huge amount of data. WWW has also affected overall growth in scientific literature that makes the process of literature review quite laborious, time consuming and cumbersome job for researchers. Hence a dire need is felt to automatically extract potential information out of immense set of scientific articles to automate the process of literature review. Therefore, in this study, aim is to present the overall progress concerning automatic information extraction from scientific articles. The information insights extracted from scientific articles are classified in two broad categories i.e. metadata and key-insights. As available benchmark datasets carry a significant role in overall development in this research domain, existing datasets against both categories are extensively reviewed. Later, research studies in literature that have applied various computational approaches applied on these datasets are consolidated. Major computational approaches in this regard include Rule-based approaches, Hidden Markov Models, Conditional Random Fields, Support Vector Machines, Naïve-Bayes classification and Deep Learning approaches. Currently, there are multiple projects going on that are focused towards the dataset construction tailored to specific information needs from scientific articles. Hence, in this study, state-of-the-art regarding information extraction from scientific articles is covered. This study also consolidates evolving datasets as well as various toolkits and code-bases that can be used for information extraction from scientific articles.

Suggested Citation

  • Zara Nasar & Syed Waqar Jaffry & Muhammad Kamran Malik, 2018. "Information extraction from scientific articles: a survey," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(3), pages 1931-1990, December.
    • Handle: RePEc:spr:scient:v:117:y:2018:i:3:d:10.1007_s11192-018-2921-5
    DOI: 10.1007/s11192-018-2921-5
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    References listed on IDEAS

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    1. Mengyang Wang & Lihe Chai, 2018. "Three new bibliometric indicators/approaches derived from keyword analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 721-750, August.
    2. Eli Cortez & Altigran S. da Silva & Marcos André Gonçalves & Filipe Mesquita & Edleno S. de Moura, 2009. "A flexible approach for extracting metadata from bibliographic citations," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(6), pages 1144-1158, June.
    3. Khalid Haruna & Maizatul Akmar Ismail & Damiasih Damiasih & Joko Sutopo & Tutut Herawan, 2017. "A collaborative approach for research paper recommender system," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-17, October.
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    Cited by:

    1. Tingting Zhang & Baozhen Lee & Qinghua Zhu & Xi Han & Ke Chen, 2023. "Document keyword extraction based on semantic hierarchical graph model," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(5), pages 2623-2647, May.
    2. Tian-Yuan Huang & Liangping Ding & Yong-Qiang Yu & Lei Huang & Liying Yang, 2023. "From AR5 to AR6: exploring research advancement in climate change based on scientific evidence from IPCC WGI reports," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(9), pages 5227-5245, September.
    3. Pengcheng Li & Wei Lu & Qikai Cheng, 2022. "Generating a related work section for scientific papers: an optimized approach with adopting problem and method information," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(8), pages 4397-4417, August.
    4. Wenhan Chao & Mengyuan Chen & Xian Zhou & Zhunchen Luo, 2023. "A joint framework for identifying the type and arguments of scientific contribution," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(6), pages 3347-3376, June.
    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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