Abstractive summarization of hospitalisation histories with transformer networks
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Abstract (arXiv)
In this paper we present a novel approach to abstractive summarization of patient hospitalisation histories. We applied an encoder-decoder framework with Longformer neural network as an encoder and BERT as a decoder. Our experiments show improved quality on some summarization tasks compared with pointer-generator networks. We also conducted a study with experienced physicians evaluating the results of our model in comparison with PGN baseline and human-generated abstracts, which showed the effectiveness of our model.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 17 of its 20 references that are in the map. First posted on arXiv 5 Apr 2022.
Builds on
17 of 20 references.
References · 17
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5 more
- 2020ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-trainingcited by 222
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Citing papers per year: 2023: 3; 2024: 1.
4 papers.
- 2023A Method to Automate the Discharge Summary Hospital Course for Neurology Patientscited by 6
- 2023A Survey for Biomedical Text Summarization: From Pre-trained to Large Language Modelscited by 3
- 2024Summarizing Radiology Reports Findings into Impressionscited by 1
- 2023Diagnosing Transformers: Illuminating Feature Spaces for Clinical Decision-Makingcited by 0
- 2024Summarizing Radiology Reports Findings into Impressions10 May 2024
- 2023Diagnosing Transformers: Illuminating Feature Spaces for Clinical Decision-Making27 May 2023
- 2023A Method to Automate the Discharge Summary Hospital Course for Neurology Patients10 May 2023
- 2023A Survey for Biomedical Text Summarization: From Pre-trained to Large Language Models18 Apr 2023