mlsanityData to 14 Oct 2026

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

arXiv 1710.06071 ↗First posted 17 Oct 2017cs.CL cs.AI stat.MLcited by 124

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Abstract (arXiv)

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.

Key facts. Cited by 124 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Oct 2017.

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Citing papers per year: 2021: 19; 2022: 12; 2023: 26; 2024: 28; 2025: 19; 2026: 20.

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38 more
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  23. 2023Farewell to Aimless Large-scale Pretraining: Influential Subset Selection for Language Modelcited by 12
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  27. 2022G-MAP: General Memory-Augmented Pre-trained Language Model for Domain Taskscited by 9
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  33. 2021Improving the robustness and accuracy of biomedical language models through adversarial trainingcited by 7
  34. 2023Can Large Language Models Follow Concept Annotation Guidelines? A Case Study on Scientific and Financial Domainscited by 7
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  38. 2025An evaluation of DeepSeek Models in Biomedical Natural Language Processingcited by 6

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