  We present PubMed 200k RCT, a new dataset based on PubMed for sequential
sentence classification. The dataset consists of approximately 200,000
abstracts of randomized controlled trials, totaling 2.3 million sentences. Each
sentence of each abstract is labeled with their role in the abstract using one
of the following classes: background, objective, method, result, or conclusion.
The purpose of releasing this dataset is twofold. First, the majority of
datasets for sequential short-text classification (i.e., classification of
short texts that appear in sequences) are small: we hope that releasing a new
large dataset will help develop more accurate algorithms for this task. Second,
from an application perspective, researchers need better tools to efficiently
skim through the literature. Automatically classifying each sentence in an
abstract would help researchers read abstracts more efficiently, especially in
fields where abstracts may be long, such as the medical field.
