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