mlsanityData to 7 Oct 2026

Attend to Medical Ontologies: Content Selection for Clinical Abstractive Summarization

arXiv 2005.00163 ↗First posted 1 May 2020cs.CLcited by 26

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Abstract (arXiv)

Sequence-to-sequence (seq2seq) network is a well-established model for text summarization task. It can learn to produce readable content; however, it falls short in effectively identifying key regions of the source. In this paper, we approach the content selection problem for clinical abstractive summarization by augmenting salient ontological terms into the summarizer. Our experiments on two publicly available clinical data sets (107,372 reports of MIMIC-CXR, and 3,366 reports of OpenI) show that our model statistically significantly boosts state-of-the-art results in terms of Rouge metrics (with improvements: 2.9% RG-1, 2.5% RG-2, 1.9% RG-L), in the healthcare domain where any range of improvement impacts patients' welfare.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 May 2020.

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Citing papers per year: 2021: 7; 2022: 8; 2023: 7; 2024: 2; 2025: 2.

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  1. 2024A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methodscited by 101
  2. 2021Pre-trained Language Models in Biomedical Domain: A Systematic Surveycited by 87
  3. 2021What's in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarizationcited by 35
  4. 2022Summarizing Patients Problems from Hospital Progress Notes Using Pre-trained Sequence-to-Sequence Modelscited by 30
  5. 2022Learning to Revise References for Faithful Summarizationcited by 28
  6. 2022Graph Enhanced Contrastive Learning for Radiology Findings Summarizationcited by 24
  7. 2021Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewardscited by 21
  8. 2021Word Graph Guided Summarization for Radiology Findingscited by 19
  9. 2022Readability Controllable Biomedical Document Summarizationcited by 19
  10. 2022A Survey on Medical Document Summarizationcited by 14
  11. 2021Question-aware Transformer Models for Consumer Health Question Summarizationcited by 11
  12. 2022MentSum: A Resource for Exploring Summarization of Mental Health Online Postscited by 11
14 more
  1. 2021TLDR9+: A Large Scale Resource for Extreme Summarization of Social Media Postscited by 10
  2. 2023Improving Biomedical Abstractive Summarisation with Knowledge Aggregation from Citation Paperscited by 9
  3. 2023Retrieving Evidence from EHRs with LLMs: Possibilities and Challengescited by 8
  4. 2023PULSAR: Pre-training with Extracted Healthcare Terms for Summarising Patients' Problems and Data Augmentation with Black-box Large Language Modelscited by 6
  5. 2022Improving Radiology Summarization with Radiograph and Anatomy Promptscited by 4
  6. 2023Curriculum-guided Abstractive Summarization for Mental Health Online Postscited by 3
  7. 2023A Survey for Biomedical Text Summarization: From Pre-trained to Large Language Modelscited by 3
  8. 2025Natural Language Generation in Healthcare: A Review of Methods and Applicationscited by 3
  9. 2022Improving the Factual Accuracy of Abstractive Clinical Text Summarization using Multi-Objective Optimizationcited by 2
  10. 2023Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysiscited by 1
  11. 2024Advancing Medical Radiograph Representation Learning: A Hybrid Pre-training Paradigm with Multilevel Semantic Granularitycited by 1
  12. 2025CSTRL: Context-Driven Sequential Transfer Learning for Abstractive Radiology Report Summarizationcited by 1
  13. 2021Learning Semi-Structured Representations of Radiology Reportscited by 0
  14. 2023Curriculum-Guided Abstractive Summarizationcited by 0