ACL-Fig: A Dataset for Scientific Figure Classification

Zeba Karishma, Shaurya Rohatgi, Kavya Shrinivas Puranik, Jian Wu, C. Lee Giles

Research output: Contribution to journalConference articlepeer-review


Most existing large-scale academic search engines are built to retrieve text-based information. However, there are no large-scale retrieval services for scientific figures and tables. One challenge for such services is understanding scientific figures’ semantics, such as their types and purposes. A key ob- stacle is the need for datasets containing annotated scientific figures and tables, which can then be used for classification, question-answering, and auto-captioning. Here, we develop a pipeline that extracts figures and tables from the scientific lit- erature and a deep-learning-based framework that classifies scientific figures using visual features. Using this pipeline, we built the first large-scale automatically annotated corpus, ACL-FIG consisting of 112,052 scientific figures extracted from ≈ 56K research papers in the ACL Anthology. The ACL-FIG-PILOT dataset contains 1,671 manually labeled scientific figures belonging to 19 categories. The dataset is ac- cessible at under a CC BY-NC license.

Original languageEnglish (US)
JournalCEUR Workshop Proceedings
StatePublished - 2023
Event2023 Workshop on Scientific Document Understanding, SDU 2023 - Virtual, Online
Duration: Feb 14 2023 → …

All Science Journal Classification (ASJC) codes

  • General Computer Science

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