The paper releases Structured PubMed: 23.2 million harmonized, section-labeled biomedical abstracts (5.9M author-structured + 17.2M LLM-labeled) mapped to PubMed IDs for training and benchmarking.
Hierarchical Neural Networks for Sequential Sentence Classification in Medical Scientific Abstracts
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RISE is an inference-time semantic reranking framework that refines low-confidence predictions in rhetorical role labeling using contrastively learned label representations, delivering an average +9.15 macro-F1 gain on hard examples across eight datasets and seven models.
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A PubMed-Scale Dataset of Structured Biomedical Abstracts
The paper releases Structured PubMed: 23.2 million harmonized, section-labeled biomedical abstracts (5.9M author-structured + 17.2M LLM-labeled) mapped to PubMed IDs for training and benchmarking.
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Semantic Reranking at Inference Time for Hard Examples in Rhetorical Role Labeling
RISE is an inference-time semantic reranking framework that refines low-confidence predictions in rhetorical role labeling using contrastively learned label representations, delivering an average +9.15 macro-F1 gain on hard examples across eight datasets and seven models.