Contextual deep learning for accurate news article categorisation with pre-trained embeddings.
Overview
- National University of Computer & Emerging Sciences (NUCES),Islamabad, Pakistan
- School of Computing, Engineering, and the Built Environment,Edinburgh Napier University, UK
Abstract
An increasing amount of online news content in digital journalism leads to novel and complicated issues regarding its classification and organisation. Systems that automate operational tasks can provide numerous advantages over systems that manually classify documents. The current work attempts to solve this problem using contextual and semantic deep learning. The text’s semantic meaning is captured using pre-trained word embeddings, and understanding is aided by a hybrid neural structure that incorporates local and distant text dependencies. This technique is compared against classical machine learning on two prominent news datasets. The deep learning approach yields a remarkable improvement in classification, reaching over 91% accuracy on the AG News corpus, a balanced four-class English benchmark, while its performance on the News Category Dataset V3, which is more complex and highly unbalanced, is considerably lower. These results highlight the effectiveness of contextual and semantic modelling for news categorisation, while also illustrating the impact of dataset complexity and class imbalance on achievable performance.
Reproduced under the paper's license (CC BY), from the paper cited above.
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Data
Datasets cited
- kaggle.com/
datasets/ , at Kaggle; found in “Data availability”mrameerhamza
Data availability
The datasets generated and/
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
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Version 1, 29 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 2 keywords, 18 references.
Cite
This paper
Hamza, A., Muhammad, A., Abbas, M. S., & Jan, S. U. (2026). Contextual deep learning for accurate news article categorisation with pre-trained embeddings. Scientific reports, 16(1), 17976. https://
BibTeX
@article{hamza2026contex
author = {Hamza, Ameer and Muhammad, Asif and Abbas, Muhammad Sohail and Jan, Sana Ullah},
title = {{Contextual deep learning for accurate news article categorisation with pre-trained embeddings}},
journal = {Scientific reports},
year = {2026},
month = apr,
volume = {16},
number = {1},
pages = {17976},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/
url = {https://
pmid = {42000781},
pmcid = {PMC13249841}
}
RIS
TY - JOUR
AU - Hamza, Ameer
AU - Muhammad, Asif
AU - Abbas, Muhammad Sohail
AU - Jan, Sana Ullah
TI - Contextual deep learning for accurate news article categorisation with pre-trained embeddings
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/
VL - 16
IS - 1
SP - 17976
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/
UR - https://
LA - en
ER -
CSL-JSON
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