mlsanityData to 14 Oct 2026

Leveraging Summary Guidance on Medical Report Summarization

arXiv 2302.04001 ↗First posted 8 Feb 2023cs.CL cs.AIcited by 8

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Abstract (arXiv)

This study presents three deidentified large medical text datasets, named DISCHARGE, ECHO and RADIOLOGY, which contain 50K, 16K and 378K pairs of report and summary that are derived from MIMIC-III, respectively. We implement convincing baselines of automated abstractive summarization on the proposed datasets with pre-trained encoder-decoder language models, including BERT2BERT, T5-large and BART. Further, based on the BART model, we leverage the sampled summaries from the train set as prior knowledge guidance, for encoding additional contextual representations of the guidance with the encoder and enhancing the decoding representations in the decoder. The experimental results confirm the improvement of ROUGE scores and BERTScore made by the proposed method, outperforming the larger model T5-large.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 30 of its 61 references that are in the map. First posted on arXiv 8 Feb 2023.

Builds on

30 of 61 references.

References · 30

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18 more
  1. 2021SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarizationcited by 407
  2. 2021QuestEval: Summarization Asks for Fact-based Evaluationcited by 244
  3. 2020ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-trainingcited by 222
  4. 2017On the Automatic Generation of Medical Imaging Reportscited by 216
  5. 2019Leveraging Pre-trained Checkpoints for Sequence Generation Taskscited by 182
  6. 2021Exploring and Distilling Posterior and Prior Knowledge for Radiology Report Generationcited by 162
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  8. 2020Factual Error Correction for Abstractive Summarization Modelscited by 108
  9. 2019Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generationcited by 102
  10. 2019Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reportscited by 96
  11. 2021Multi-modal Understanding and Generation for Medical Images and Text via Vision-Language Pre-Trainingcited by 96
  12. 2018Learning to Summarize Radiology Findingscited by 54
  13. 2022FactPEGASUS: Factuality-Aware Pre-training and Fine-tuning for Abstractive Summarizationcited by 49
  14. 2018Natural Language Generation for Electronic Health Recordscited by 27
  15. 2022Towards Improving Faithfulness in Abstractive Summarizationcited by 27
  16. 2021Automated Generation of Accurate & Fluent Medical X-ray Reportscited by 24
  17. 2021Towards Clinical Encounter Summarization: Learning to Compose Discharge Summaries from Prior Notescited by 19
  18. 2022Salience Allocation as Guidance for Abstractive Summarizationcited by 14

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 3; 2025: 1; 2026: 1.

8 papers.

  1. 2023Context Aware Query Rewriting for Text Rankers using LLMcited by 14
  2. 2024Exploring the Efficacy of Large Language Models in Summarizing Mental Health Counseling Sessions: A Benchmark Studycited by 13
  3. 2023Query Understanding in the Age of Large Language Modelscited by 7
  4. 2024CTISum: A New Benchmark Dataset For Cyber Threat Intelligence Summarizationcited by 2
  5. 2025Large Language Model-Based Generation of Discharge Summariescited by 2
  6. 2023Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysiscited by 1
  7. 2024Intelligent Multi-Document Summarisation for Extracting Insights on Racial Inequalities from Maternity Incident Investigation Reportscited by 0
  8. 2026Toward Complete Hospital Discharge Summarization with Abstract Meaning Representationcited by 0