URL Extraction from Scholarly Documents: A Cross-Format Comparative Analysis

Document Type

Conference Paper

Publication Date

2026

DOI

10.1145/3805696.3846023

Publication Title

JCDL '26: Proceedings of the 26th ACM/IEEE Joint Conference on Digital Libraries

Pages

Article 17 (1-11)

Conference Name

JCDL '26: The 2026 ACM/IEEE Joint Conference on Digital Libraries, Frisco, USA, October 13-16, 2026

Abstract

URLs in scholarly documents link to rich external resources such as datasets, software, publications, and websites. Extracting these URLs is crucial in the data preparation stage of many downstream tasks, such as link rot analysis, web crawling, and building knowledge graphs. However, existing studies often downplay this phase, simply extracting URLs from a single format, usually text directly converted from PDFs. We present a systematic study evaluating URL extraction across six input formats (text with annotation layer, LaTeX, HTML, XML, Markdown, and PNG converted from PDF). To support the evaluation, we compiled a benchmark dataset consisting of 2,338 manually annotated URLs from 200 arXiv papers spanning a wide range of domains over a 33-year period. In addition to evaluating individual file formats, we also compared 63 composite input-format combinations. Our extensive evaluations indicate that TEXTWAL achieves the best performance among single-format inputs, while TEXTWAL+LaTeX achieves the best overall URL extraction performance. The same trend is observed for URLs linking to open-access datasets and software. To further validate these findings, we apply our format-specific URL extraction pipelines to a longitudinal random sample of 364,744 arXiv papers spanning 33 years. We observe a sharp increase in URL density after 2015, along with remarkable differences in URL extraction across file formats over time. Overall, our study highlights the importance of selecting an appropriate format for URL extraction from scholarly documents. The dataset and code are publicly available at: https://github.com/lamps-lab/arxiv-url-bench.

Rights

© 2026 The Authors.

Published under the terms of a Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Original Publication Citation

Obadage, R. R., Salsabil, L., Alam, S., Ingram, W., Banerjee, B., Fox, E., & Wu, J. (2026). URL extraction from scholarly documents: A cross-format comparative analysis. In Haihua Chen, Jim Jansen, Jiqun Liu, Mark V. Albert, Junhua Ding, & Jiangping Chen (Eds.), JCDL '26: Proceedings of the 26th ACM/IEEE Joint Conference on Digital Libraries (1-11). Association for Computing Machinery. https://doi.org/10.1145/3805696.3846023

ORCID

0000-0003-1593-4052 (Obadage), 0000-0002-6162-2896 (Salsabil), 0000-0003-0173-4463 (Wu)

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