학술논문 내에서 참고문헌 정보가 포함된 서지 메타데이터 자동 생성 연구 (Automatic Generation of Bibliographic Metadata with Reference Information for Academic Journals)

정선기 (Seonki Jeong); 신현호 (shin hyeon ho); 지선영 (Seon-Yeong Ji); 최성필 (Sung-Pil Choi)

doi:10.4275/KSLIS.2022.56.3.241

Automatic Generation of Bibliographic Metadata with Reference Information for Academic Journals

Journal of the Korean Society for Library and Information Science
2022, 56(3), pp.241-264
DOI : 10.4275/KSLIS.2022.56.3.241
Publisher : 한국문헌정보학회
Research Area : Interdisciplinary Studies > Library and Information Science
Received : July 17, 2022
Accepted : August 15, 2022
Published : August 31, 2022

Seonki Jeong ¹, shin hyeon ho ², Seon-Yeong Ji ³, Sung-Pil Choi ²

¹경기대학교 문헌정보학과
²경기대학교
³보인정보기술

Excellent Accredited

ABSTRACT

Bibliographic metadata can help researchers effectively utilize essential publications that they need and grasp academic trends of their own fields. With the manual creation of the metadata costly and time-consuming. it is nontrivial to effectively automatize the metadata construction using rule-based methods due to the immoderate variety of the article forms and styles according to publishers and academic societies. Therefore, this study proposes a two-step extraction process based on rules and deep neural networks for generating bibliographic metadata of scientific articlles to overcome the difficulties above. The extraction target areas in articles were identified by using a deep neural network-based model, and then the details in the areas were analyzed and sub-divided into relevant metadata elements. IThe proposed model also includes a model for generating reference summary information, which is able to separate the end of the text and the starting point of a reference, and to extract individual references by essential rule set, and to identify all the bibliographic items in each reference by a deep neural network. In addition, in order to confirm the possibility of a model that generates the bibliographic information of academic papers without pre- and post-processing, we conducted an in-depth comparative experiment with various settings and configurations. As a result of the experiment, the method proposed in this paper showed higher performance.

KEYWORDS

NLP, Information Extraction, Reference Extraction, Metadata Extraction, Language Model

Citation status

* References for papers published after 2023 are currently being built.

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Journal of the Korean Society for Library and Information Science 2023 KCI Impact Factor : 0.84

Automatic Generation of Bibliographic Metadata with Reference Information for Academic Journals

ABSTRACT

KEYWORDS

Citation status

* References for papers published after 2023 are currently being built.

Journal of the Korean Society for Library and Information Science 2023 KCI Impact Factor : 0.84

Automatic Generation of Bibliographic Metadata with Reference Information for Academic Journals

ABSTRACT

KEYWORDS

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* References for papers published after 2023 are currently being built.