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

PubMed 200k RCT: a Dataset for Sequential Sentence Classification in Medical Abstracts