# Ontology-Aware Clinical Abstractive Summarization

arXiv: https://arxiv.org/abs/1905.05818 · HTML: https://mlsanity.com/paper/1905.05818/

## Key facts

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

## Built on by (most cited first, 30 of 33)

- [ChatGPT Makes Medicine Easy to Swallow: An Exploratory Case Study on Simplified Radiology Reports](https://mlsanity.com/paper/2212.14882/) (2022)
- [What's in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarization](https://mlsanity.com/paper/2105.00816/) (2021)
- [Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review](https://mlsanity.com/paper/2107.02975/) (2021)
- [Human Evaluation and Correlation with Automatic Metrics in Consultation Note Generation](https://mlsanity.com/paper/2204.00447/) (2022)
- [PriMock57: A Dataset Of Primary Care Mock Consultations](https://mlsanity.com/paper/2204.00333/) (2022)
- [Summarizing Patients Problems from Hospital Progress Notes Using Pre-trained Sequence-to-Sequence Models](https://mlsanity.com/paper/2208.08408/) (2022)
- [Learning to Revise References for Faithful Summarization](https://mlsanity.com/paper/2204.10290/) (2022)
- [Graph Enhanced Contrastive Learning for Radiology Findings Summarization](https://mlsanity.com/paper/2204.00203/) (2022)
- [Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards](https://mlsanity.com/paper/2107.00176/) (2021)
- [Towards Clinical Encounter Summarization: Learning to Compose Discharge Summaries from Prior Notes](https://mlsanity.com/paper/2104.13498/) (2021)
- [Word Graph Guided Summarization for Radiology Findings](https://mlsanity.com/paper/2112.09925/) (2021)
- [A Survey on Medical Document Summarization](https://mlsanity.com/paper/2212.01669/) (2022)
- [A Meta-Evaluation of Faithfulness Metrics for Long-Form Hospital-Course Summarization](https://mlsanity.com/paper/2303.03948/) (2023)
- [Hierarchical Annotation for Building A Suite of Clinical Natural Language Processing Tasks: Progress Note Understanding](https://mlsanity.com/paper/2204.03035/) (2022)
- [User-Driven Research of Medical Note Generation Software](https://mlsanity.com/paper/2205.02549/) (2022)
- [Question-aware Transformer Models for Consumer Health Question Summarization](https://mlsanity.com/paper/2106.00219/) (2021)
- [MentSum: A Resource for Exploring Summarization of Mental Health Online Posts](https://mlsanity.com/paper/2206.00856/) (2022)
- [TLDR9+: A Large Scale Resource for Extreme Summarization of Social Media Posts](https://mlsanity.com/paper/2110.01159/) (2021)
- [Generating medically-accurate summaries of patient-provider dialogue: A multi-stage approach using large language models](https://mlsanity.com/paper/2305.05982/) (2023)
- [PULSAR: Pre-training with Extracted Healthcare Terms for Summarising Patients' Problems and Data Augmentation with Black-box Large Language Models](https://mlsanity.com/paper/2306.02754/) (2023)
- [Improving Radiology Summarization with Radiograph and Anatomy Prompts](https://mlsanity.com/paper/2210.08303/) (2022)
- [Conceptualizing Machine Learning for Dynamic Information Retrieval of Electronic Health Record Notes](https://mlsanity.com/paper/2308.08494/) (2023)
- [Exploring Optimal Granularity for Extractive Summarization of Unstructured Health Records: Analysis of the Largest Multi-Institutional Archive of Health Records in Japan](https://mlsanity.com/paper/2209.10041/) (2022)
- [Curriculum-guided Abstractive Summarization for Mental Health Online Posts](https://mlsanity.com/paper/2302.00954/) (2023)
- [Neural Summarization of Electronic Health Records](https://mlsanity.com/paper/2305.15222/) (2023)
- [Aspect-oriented Consumer Health Answer Summarization](https://mlsanity.com/paper/2405.06295/) (2024)
- [Improving the Factual Accuracy of Abstractive Clinical Text Summarization using Multi-Objective Optimization](https://mlsanity.com/paper/2204.00797/) (2022)
- [Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysis](https://mlsanity.com/paper/2307.12803/) (2023)
- [RadBARTsum: Domain Specific Adaption of Denoising Sequence-to-Sequence Models for Abstractive Radiology Report Summarization](https://mlsanity.com/paper/2406.03062/) (2024)
- [CSTRL: Context-Driven Sequential Transfer Learning for Abstractive Radiology Report Summarization](https://mlsanity.com/paper/2503.05750/) (2025)

## 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.