Letting the Data Speak: Extracting Keywords from Crowdsourced Collections with AI
This research examines the technical and ethical challenges of automated keyword extraction in crowdsourced collections, using the University of Oxford's Second World War archive as a case study. It compares three NLP approaches and finds that while promising, no method is perfect; open-weight extractive models are recommended over generative AI for responsible deployment.
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[Submitted on 10 Jul 2026]
Title:Letting the Data Speak: Extracting Keywords from Crowdsourced Collections with AI
View a PDF of the paper titled Letting the Data Speak: Extracting Keywords from Crowdsourced Collections with AI, by Miguel Arana-Catania and 2 other authors
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Abstract:Identifying and assigning keywords at scale is a technical, practical, and ethical challenge for crowdsourced collections. This article reports the findings of the "Extracting Keywords from Crowdsourced Collections" project, which used the Their Finest Hour Online Archive, a crowdsourced Second World War digital collection hosted by the University of Oxford, as a case study. The project evaluated three Natural Language Processing approaches to automate keyword extraction: Named Entity Recognition, Keyword Extraction, and Topic Modelling. It tested these approaches across a range of artificial intelligence techniques, from traditional statistical methods to modern GenAI neural networks. Our quantitative and qualitative findings indicate that Natural Language Processing approaches offer real potential for keyword extraction at scale in crowdsourced collections, but that no single method offers a complete solution and that model choice significantly shapes results. We argue that in crowdsourced collections, where metadata is the direct product of engagement with living contributors, automated keyword extraction raises distinct stewardship responsibilities that must be addressed alongside technical performance. Open-weight, extractive models emerge from our evaluation as best placed to support responsible deployment, while generative AI, despite its abstractive potential, introduces accountability risks that anyone managing crowdsourced collections should weigh carefully.
Comments: 45 pages, 6 tables
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Digital Libraries (cs.DL); Information Retrieval (cs.IR); Machine Learning (cs.LG)
Cite as: arXiv:2607.09324 [cs.CL]
(or arXiv:2607.09324v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.09324
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Miguel Arana-Catania [view email] [v1] Fri, 10 Jul 2026 12:06:18 UTC (501 KB)
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