MeSHup: A Corpus for Full Text Biomedical Document Indexing
This work addresses the time-consuming and costly manual indexing of biomedical articles in PubMed, providing a resource to assist human curators, though it is incremental as it builds on existing datasets and methods.
The authors tackled the problem of assigning Medical Subject Headings (MeSH) to biomedical documents by releasing MeSHup, a large-scale annotated corpus of 1,342,667 full-text articles, and trained an end-to-end model to establish a new baseline for computational indexing.
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.