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A Survey on Medical Document Summarization

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arxiv 2212.01669 v1 pith:KYJ6N42S submitted 2022-12-03 cs.CL

Raghav Jain , Anubhav Jangra , Sriparna Saha , Adam Jatowt This is my paper · ORCID
classification cs.CL
keywords medicaldatasummarizationsurveyaccessiblealikeallowingbecome
verification ladder T0 review T1 audit T2 compute T3 formal
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The internet has had a dramatic effect on the healthcare industry, allowing documents to be saved, shared, and managed digitally. This has made it easier to locate and share important data, improving patient care and providing more opportunities for medical studies. As there is so much data accessible to doctors and patients alike, summarizing it has become increasingly necessary - this has been supported through the introduction of deep learning and transformer-based networks, which have boosted the sector significantly in recent years. This paper gives a comprehensive survey of the current techniques and trends in medical summarization

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. CLI-RAG: A Retrieval-Augmented Framework for Clinically Structured and Context Aware Text Generation with LLMs

    cs.CL 2025-07 reject novelty 4.0 of 10

    CLI-RAG uses two-stage retrieval over hierarchically chunked EHR notes to generate SOAP progress notes, but its headline 87.7% temporal alignment result is inconsistent across models.

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