CRAWLDoc: A Dataset for Robust Ranking of Bibliographic Documents

Fabian Karl, Ansgar Scherp
6/4/2025
cs.CLcs.IR
benchmark-dataset

Official code: FKarl/CRAWLDoc (5 stars, CC0-1.0, last commit 2025-01-23).

Abstract

Publication databases rely on accurate metadata extraction from diverse web sources, yet variations in web layouts and data formats present challenges for metadata providers. This paper introduces CRAWLDoc, a new method for contextual ranking of linked web documents. Starting with a publication's URL, such as a digital object identifier, CRAWLDoc retrieves the landing page and all linked web resources, including PDFs, ORCID profiles, and supplementary materials. It embeds these resources, along with anchor texts and the URLs, into a unified representation. For evaluating CRAWLDoc, we have created a new, manually labeled dataset of 600 publications from six top publishers in computer science. Our method CRAWLDoc demonstrates a robust and layout-independent ranking of relevant documents across publishers and data formats. It lays the foundation for improved metadata extraction from web documents with various layouts and formats. Our source code and dataset can be accessed at https://github.com/FKarl/CRAWLDoc.

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Code Implementations(1)

FKarl/CRAWLDocOfficial100%
CC0-1.0

This repository contains code to reproduce the results from our paper "CRAWLDoc: A Dataset for Robust Ranking of Bibliographic Documents"

50Shell, PythonJan 21, 20251 years agoCC0-1.0
bibliographic-datainformation-retrievallanguage-model

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Cite this paper

@article{karl2025crawldoc,
  title  = {CRAWLDoc: A Dataset for Robust Ranking of Bibliographic Documents},
  author = {Fabian Karl and Ansgar Scherp},
  year   = {2025},
  eprint = {2506.03822},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2506.03822v1}
}

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