A Large-Scale Dataset for Biomedical Keyphrase Generation
원문 초록
Keyphrase generation is the task consisting in generating a set of words or phrases that highlight the main topics of a document. There are few datasets for keyphrase generation in the biomedical domain and they do not meet the expectations in terms of size for training generative models. In this paper, we introduce kp-biomed, the first large-scale biomedical keyphrase generation dataset with more than 5M documents collected from PubMed abstracts. We train and release several generative models and conduct a series of experiments showing that using large scale datasets improves significantly the performances for present and absent keyphrase generation. The dataset is available under CC-BY-NC v4.0 license at https://huggingface.co/ datasets/taln-ls2n/kpbiomed.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
An Analysis of Datasets, Metrics and Models in Keyphrase Generation
ERU-KG: Efficient Reference-aligned Unsupervised Keyphrase Generation
Self-Compositional Data Augmentation for Scientific Keyphrase Generation
One2set + Large Language Model: Best Partners for Keyphrase Generation
Unsupervised Domain Adaptation for Keyphrase Generation using Citation Contexts
MetaKP: On-Demand Keyphrase Generation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.