MeSHup: A Corpus for Full Text Biomedical Document Indexing

TL;DR

This paper introduces MeSHup, a large-scale annotated corpus of over 1.3 million biomedical articles with MeSH labels, enabling improved development and evaluation of automated indexing systems.

Contribution

The paper provides the first publicly available, extensive corpus for biomedical document indexing and establishes a new baseline using an end-to-end model.

Findings

The corpus contains 1,342,667 articles with labels and metadata.

An end-to-end model trained on MeSHup outperforms previous methods.

The dataset facilitates robust evaluation and comparison of indexing systems.

Abstract

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.