UGDAS: Unsupervised graph-network based denoiser for abstractive summarization in biomedical domain.

• A novel model called UGDAS is proposed for the biomedical-domain abstractive summarization task. • The model is consisted of a graph-network based denoiser and an auto-regressive generator, and it is more effective to capture the sentence relationship. • The denoiser utilizes domain knowledge to represent the rich information of the sentence in the biomedical domain. • The ranking process is optimized by the integration of sentence position information. • Our model achieves the state-of-the-art result on a recently-introduced CORD-19 dataset and outperforms the related abstractive models on the PubMed dataset. Abstractive summarization models can generate summary auto-regressively, but the quality is often impacted by the noise in the text. Learning cross-sentence relations is a crucial step in this task and the graph-based network is more effective to capture the sentence relationship. Moreover, knowledge is very important to distinguish the noise of the text in special domain. A novel model structure called UGDAS is proposed in this paper, which combines a sentence-level denoiser based on an unsupervised graph-network and an auto-regressive generator. It utilizes domain knowledge and sentence position information to denoise the original text and further improve the quality of generated summaries. We use the recently-introduced dataset CORD-19 (COVID-19 Open Research Dataset) on text summarization task, which contains large-scale data on coronaviruses. The experimental results show that our model achieves the SOTA (state-of-the-art) result on CORD-19 dataset and outperforms the related baseline models on the PubMed Abstract dataset. [ABSTRACT FROM AUTHOR]