Evidence map›Paper›PMID 34179900›Full record

ArticleProceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting2021

What's in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarization.

Griffin Adams, Emily Alsentzer, Mert Ketenci, Jason Zucker, Noémie Elhadad

Abstract read
In one paragraph

Article in Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed.

  1. Article
  2. Toward Complete Hospital Discharge Summarization with Abstract Meaning Representation.Proceedings. IEEE International Conference on Healthcare Informatics · 2026
    Article
  3. Article
  4. Large Language Models with Temporal Reasoning for Longitudinal Clinical Summarization and Prediction.Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing · 2025
    Article
  5. Observational
  6. Article
  7. Article
  8. A dataset and benchmark for hospital course summarization with adapted large language models.Journal of the American Medical Informatics Association : JAMIA · 2025
    Article
  9. Article
  10. Retrieving Evidence from EHRs with LLMs: Possibilities and Challenges.Proceedings of machine learning research · 2024
    Article
  11. Article
  12. A method to automate the discharge summary hospital course for neurology patients.Journal of the American Medical Informatics Association : JAMIA · 2023
    Article
  13. Article
  14. Generating EDU Extracts for Plan-Guided Summary Re-Ranking.Proceedings of the conference. Association for Computational Linguistics. Meeting · 2023
    Article
  15. What are the Desired Characteristics of Calibration Sets? Identifying Correlates on Long Form Scientific Summarization.Proceedings of the conference. Association for Computational Linguistics. Meeting · 2023
    Article
  16. Article
  17. Article
  18. Summarizing Patients' Problems from Hospital Progress Notes Using Pre-trained Sequence-to-Sequence Models.Proceedings of COLING. International Conference on Computational Linguistics · 2022
    Article
  19. Article
  20. Hierarchical Annotation for Building A Suite of Clinical Natural Language Processing Tasks: Progress Note Understanding.LREC ... International Conference on Language Resources & Evaluation : [proceedings]. International Conference on Language Resources & Evaluation · 2022
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Griffin AdamsColumbia University, New York, NY.
Emily AlsentzerHarvard-MIT's Health Science and Technology, Cambridge, MA.
Mert KetenciColumbia University, New York, NY.
Jason ZuckerColumbia University, New York, NY.
Noémie ElhadadColumbia University, New York, NY.

Funding

Open Health Natural Language Processing CollaboratoryU01TR002062 · NCATS · MAYO CLINIC ROCHESTER · PI JIANG, XIAOQIAN, LIU, HONGFANG · 2017 to 2021
$7.6M
Extended Methods and Software Development for Health NLPR01GM114355 · NIGMS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI BETHARD, STEVEN, SAVOVA, GUERGANA K. · 2016 to 2024
$5.0M
NCATS NIH HHS U01 TR002062NIGMS NIH HHS R01 GM114355
6 · The paper itself

Abstract

Summarization of clinical narratives is a long-standing research problem. Here, we introduce the task of hospital-course summarization. Given the documentation authored throughout a patient's hospitalization, generate a paragraph that tells the story of the patient admission. We construct an English, text-to-text dataset of 109,000 hospitalizations (2M source notes) and their corresponding summary proxy: the clinician-authored "Brief Hospital Course" paragraph written as part of a discharge note. Exploratory analyses reveal that the BHC paragraphs are highly abstractive with some long extracted fragments; are concise yet comprehensive; differ in style and content organization from the source notes; exhibit minimal lexical cohesion; and represent silver-standard references. Our analysis identifies multiple implications for modeling this complex, multi-document summarization task.

Identifiers

PMID34179900
PMCPMC8225248

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.