  Medical Subject Heading (MeSH) indexing refers to the problem of assigning a
given biomedical document with the most relevant labels from an extremely large
set of MeSH terms. Currently, the vast number of biomedical articles in the
PubMed database are manually annotated by human curators, which is time
consuming and costly; therefore, a computational system that can assist the
indexing is highly valuable. When developing supervised MeSH indexing systems,
the availability of a large-scale annotated text corpus is desirable. A
publicly available, large corpus that permits robust evaluation and comparison
of various systems is important to the research community. We release a large
scale annotated MeSH indexing corpus, MeSHup, which contains 1,342,667 full
text articles in English, together with the associated MeSH labels and
metadata, authors, and publication venues that are collected from the MEDLINE
database. We train an end-to-end model that combines features from documents
and their associated labels on our corpus and report the new baseline.
