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Currently, Medical Subject Headings (MeSH) are manually assigned to everybiomedical article published and subsequently recorded in the PubMed databaseto facilitate retrieving relevant information. With the rapid growth of thePubMed database, large-scale biomedical document indexing becomes increasinglyimportant. MeSH indexing is a challenging task for machine learning, as itneeds to assign multiple labels to each article from an extremely largehierachically organized collection. To address this challenge, we proposeKenMeSH, an end-to-end model that combines new text features and a dynamic\textbf{K}nowledge-\textbf{en}hanced mask attention that integrates documentfeatures with MeSH label hierarchy and journal correlation features to indexMeSH terms. Experimental results show the proposed method achievesstate-of-the-art performance on a number of measures.

KenMeSH: Knowledge-enhanced End-to-end Biomedical Text Labelling