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Ontology-Aware Clinical Abstractive Summarization

arXiv 1905.05818 ↗First posted 14 May 2019cs.CL cs.IRcited by 33

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Abstract (arXiv)

Automatically generating accurate summaries from clinical reports could save a clinician's time, improve summary coverage, and reduce errors. We propose a sequence-to-sequence abstractive summarization model augmented with domain-specific ontological information to enhance content selection and summary generation. We apply our method to a dataset of radiology reports and show that it significantly outperforms the current state-of-the-art on this task in terms of rouge scores. Extensive human evaluation conducted by a radiologist further indicates that this approach yields summaries that are less likely to omit important details, without sacrificing readability or accuracy.

Key facts. Cited by 33 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 May 2019.

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Citing papers per year: 2021: 8; 2022: 13; 2023: 9; 2024: 2; 2025: 1.

33 papers.

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  1. 2022ChatGPT Makes Medicine Easy to Swallow: An Exploratory Case Study on Simplified Radiology Reportscited by 101
  2. 2021What's in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarizationcited by 35
  3. 2021Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Reviewcited by 35
  4. 2022Human Evaluation and Correlation with Automatic Metrics in Consultation Note Generationcited by 33
  5. 2022PriMock57: A Dataset Of Primary Care Mock Consultationscited by 31
  6. 2022Summarizing Patients Problems from Hospital Progress Notes Using Pre-trained Sequence-to-Sequence Modelscited by 30
  7. 2022Learning to Revise References for Faithful Summarizationcited by 28
  8. 2022Graph Enhanced Contrastive Learning for Radiology Findings Summarizationcited by 24
  9. 2021Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewardscited by 22
  10. 2021Towards Clinical Encounter Summarization: Learning to Compose Discharge Summaries from Prior Notescited by 19
  11. 2021Word Graph Guided Summarization for Radiology Findingscited by 19
  12. 2022A Survey on Medical Document Summarizationcited by 14
21 more
  1. 2023A Meta-Evaluation of Faithfulness Metrics for Long-Form Hospital-Course Summarizationcited by 14
  2. 2022Hierarchical Annotation for Building A Suite of Clinical Natural Language Processing Tasks: Progress Note Understandingcited by 13
  3. 2022User-Driven Research of Medical Note Generation Softwarecited by 13
  4. 2021Question-aware Transformer Models for Consumer Health Question Summarizationcited by 11
  5. 2022MentSum: A Resource for Exploring Summarization of Mental Health Online Postscited by 11
  6. 2021TLDR9+: A Large Scale Resource for Extreme Summarization of Social Media Postscited by 10
  7. 2023Generating medically-accurate summaries of patient-provider dialogue: A multi-stage approach using large language modelscited by 7
  8. 2023PULSAR: Pre-training with Extracted Healthcare Terms for Summarising Patients' Problems and Data Augmentation with Black-box Large Language Modelscited by 6
  9. 2022Improving Radiology Summarization with Radiograph and Anatomy Promptscited by 4
  10. 2023Conceptualizing Machine Learning for Dynamic Information Retrieval of Electronic Health Record Notescited by 4
  11. 2022Exploring Optimal Granularity for Extractive Summarization of Unstructured Health Records: Analysis of the Largest Multi-Institutional Archive of Health Records in Japancited by 3
  12. 2023Curriculum-guided Abstractive Summarization for Mental Health Online Postscited by 3
  13. 2023Neural Summarization of Electronic Health Recordscited by 3
  14. 2024Aspect-oriented Consumer Health Answer Summarizationcited by 3
  15. 2022Improving the Factual Accuracy of Abstractive Clinical Text Summarization using Multi-Objective Optimizationcited by 2
  16. 2023Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysiscited by 1
  17. 2024RadBARTsum: Domain Specific Adaption of Denoising Sequence-to-Sequence Models for Abstractive Radiology Report Summarizationcited by 1
  18. 2025CSTRL: Context-Driven Sequential Transfer Learning for Abstractive Radiology Report Summarizationcited by 1
  19. 2021Learning Semi-Structured Representations of Radiology Reportscited by 0
  20. 2023Curriculum-Guided Abstractive Summarizationcited by 0
  21. 2023Is In-hospital Meta-information Useful for Abstractive Discharge Summary Generation?cited by 0