A Fine-grained citation graph for biomedical academic papers: the finding-citation graph

Yuan Liang, Massimo Poesio, Roonak Rezvani

Abstract
Citations typically mention findings as well as papers. To model this richer notion of citation, we introduce a richer form of citation graph with nodes for both academic papers and their findings: the finding-citation graph (FCG). We also present a new pipeline to construct such a graph, which includes a finding identification module and a citation sentence extraction module. From each paper, it extracts rich basic information, abstract, and structured full text first. The abstract and vital sections, such as the results and discussion, are input into the finding identification module. This module identifies multiple findings from a paper, achieving an 80% accuracy in multiple findings evaluation. The full text is input into the citation sentence extraction module to identify inline citation sentences and citation markers, achieving 97.7% accuracy. Then, the graph is constructed using the outputs from the two modules mentioned above. We used the Europe PMC to build such a graph using the pipeline, resulting in a graph with 14.25 million nodes and 76 million edges.
Anthology ID:
2024.bionlp-1.33
Volume:
Proceedings of the 23rd Workshop on Biomedical Natural Language Processing
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Dina Demner-Fushman, Sophia Ananiadou, Makoto Miwa, Kirk Roberts, Junichi Tsujii
Venues:
BioNLP | WS
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
416–426
Language:
URL:
https://aclanthology.org/2024.bionlp-1.33
DOI:
10.18653/v1/2024.bionlp-1.33
Bibkey:
Cite (ACL):
Yuan Liang, Massimo Poesio, and Roonak Rezvani. 2024. A Fine-grained citation graph for biomedical academic papers: the finding-citation graph. In Proceedings of the 23rd Workshop on Biomedical Natural Language Processing, pages 416–426, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
A Fine-grained citation graph for biomedical academic papers: the finding-citation graph (Liang et al., BioNLP-WS 2024)
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