CRAWLDoc: A Dataset for Robust Ranking of Bibliographic Documents
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.
6841be30ea0023757b685a3b
Fabian Karl
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2025-06-05T17:07:58.514Z
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Fabian Karl
user
FabianKarl
6841be30ea0023757b685a3c
Ansgar Scherp
CRAWLDoc: A Dataset for Robust Ranking of Bibliographic Documents
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.
CRAWLDoc is a method for ranking linked web documents by embedding resources and metadata into a unified representation, enabling robust metadata extraction across diverse web layouts and formats.
contextual ranking linked web documents CRAWLDoc landing page PDFs ORCID profiles supplementary materials unified representation anchor texts URL metadata extraction layout-independent ranking