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Award ID contains: 2145411

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  1. ; (, Zenodo)
    PubMed Graph Benchmark (PGB) aggregates the metadata associated with the biomedical articles from PubMed into a unified source. The benchmark contains metadata including title, abstract, authors, in/out citations, MeSH terms, MeSH hierarchy, venue, publication type, and chemicals.</p> 
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  2. ; ; (, CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management)
    With the ever-increasing abundance of biomedical articles, improving the accuracy of keyword search results becomes crucial for ensuring reproducible research. However, keyword extraction for biomedical articles is hard due to the existence of obscure keywords and the lack of a comprehensive benchmark. PubMedAKE is an author-assigned keyword extraction dataset that contains the title, abstract, and keywords of over 843,269 articles from the PubMed open access subset database. This dataset, publicly available on Zenodo, is the largest keyword extraction benchmark with sufficient samples to train neural networks. Experimental results using state-of-the-art baseline methods illustrate the need for developing automatic keyword extraction methods for biomedical literature. 
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    Full Text Available 
  3. ; ; (, Proceedings of the 9th Machine Learning for Healthcare Conference)
    Free, publicly-accessible full text available August 30, 2026
  4. ; ; ; ; ; (, Proceedings of the 4th Machine Learning for Health Symposium)
    Free, publicly-accessible full text available December 15, 2025
  5. ; ; ; (, ACM)
    Full Text Available 
  6. ; ; ; ; ; (, ACM)
    Full Text Available 
  7. ; ; ; ; ; (, ACM)
    Full Text Available 
  8. ; ; ; ; ; ; ; (, Proceedings of the fifth Conference on Health, Inference, and Learning)
    Accepted Manuscript Full Text Available
  9. ; ; ; ; ; (, The Twelfth International Conference on Learning Representations)
    Accepted Manuscript Full Text Available
  10. ; ; (, Proceedings of the 27th International Conference on Extending Database Technology)
    Accepted Manuscript Full Text Available