# CLIP: A Dataset for Extracting Action Items for Physicians from Hospital Discharge Notes

arXiv: https://arxiv.org/abs/2106.02524 · HTML: https://mlsanity.com/paper/2106.02524/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 46 references that are in the map.
- First posted on arXiv 4 Jun 2021.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [BERT Rediscovers the Classical NLP Pipeline](https://mlsanity.com/paper/1905.05950/) (2019)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [emrQA: A Large Corpus for Question Answering on Electronic Medical Records](https://mlsanity.com/paper/1809.00732/) (2018)
- [Hurtful Words: Quantifying Biases in Clinical Contextual Word Embeddings](https://mlsanity.com/paper/2003.11515/) (2020)
- [WikiAsp: A Dataset for Multi-domain Aspect-based Summarization](https://mlsanity.com/paper/2011.07832/) (2020)
- [Pretrained Language Models for Sequential Sentence Classification](https://mlsanity.com/paper/1909.04054/) (2019)
- [BioSentVec: creating sentence embeddings for biomedical texts](https://mlsanity.com/paper/1810.09302/) (2018)
- [CheXpert++: Approximating the CheXpert labeler for Speed,Differentiability, and Probabilistic Output](https://mlsanity.com/paper/2006.15229/) (2020)
- [Phenotyping of Clinical Notes with Improved Document Classification Models Using Contextualized Neural Language Models](https://mlsanity.com/paper/1910.13664/) (2019)
- [Students Need More Attention: BERT-based AttentionModel for Small Data with Application to AutomaticPatient Message Triage](https://mlsanity.com/paper/2006.11991/) (2020)
- [Self-Supervised Learning for Contextualized Extractive Summarization](https://mlsanity.com/paper/1906.04466/) (2019)
- [Unsupervised Pseudo-Labeling for Extractive Summarization on Electronic Health Records](https://mlsanity.com/paper/1811.08040/) (2018)

## Built on by (most cited first, 20 of 20)

- [Do We Still Need Clinical Language Models?](https://mlsanity.com/paper/2302.08091/) (2023)
- [From Text to Transformation: A Comprehensive Review of Large Language Models' Versatility](https://mlsanity.com/paper/2402.16142/) (2024)
- [Hierarchical Annotation for Building A Suite of Clinical Natural Language Processing Tasks: Progress Note Understanding](https://mlsanity.com/paper/2204.03035/) (2022)
- [A Scoping Review of Publicly Available Language Tasks in Clinical Natural Language Processing](https://mlsanity.com/paper/2112.05780/) (2021)
- [A Method to Automate the Discharge Summary Hospital Course for Neurology Patients](https://mlsanity.com/paper/2305.06416/) (2023)
- [Summarizing Speech: A Comprehensive Survey](https://mlsanity.com/paper/2504.08024/) (2025)
- [Learning to translate by learning to communicate](https://mlsanity.com/paper/2207.07025/) (2022)
- [MedDec: A Dataset for Extracting Medical Decisions from Discharge Summaries](https://mlsanity.com/paper/2408.12980/) (2024)
- [Are Clinical T5 Models Better for Clinical Text?](https://mlsanity.com/paper/2412.05845/) (2024)
- [Responsible AI Considerations in Text Summarization Research: A Review of Current Practices](https://mlsanity.com/paper/2311.11103/) (2023)
- [Medmarks: A Comprehensive Open-Source LLM Benchmark Suite for Medical Tasks](https://mlsanity.com/paper/2605.01417/) (2026)
- [Checkup2Action: A Multimodal Clinical Check-up Report Dataset for Patient-Oriented Action Card Generation](https://mlsanity.com/paper/2605.11533/) (2026)
- [Three Generations of Healthcare IT: From the Digital Record to the Computable Care Process](https://mlsanity.com/paper/2608.08806/) (2026)
- [MED-SE: Medical Entity Definition-based Sentence Embedding](https://mlsanity.com/paper/2212.04734/) (2022)
- [Meeting Action Item Detection with Regularized Context Modeling](https://mlsanity.com/paper/2303.16763/) (2023)
- [Don't Pay Attention, PLANT It: Pretraining Attention via Learning-to-Rank](https://mlsanity.com/paper/2410.23066/) (2024)
- [Training-Free Adaptation of New-Generation LLMs using Legacy Clinical Models](https://mlsanity.com/paper/2601.03423/) (2026)
- [Systematic Evaluation of Large Language Models for Post-Discharge Clinical Action Extraction](https://mlsanity.com/paper/2605.06191/) (2026)
- [Clinical Intent Extraction: A FHIR-Aligned Representation and the CIRCA Benchmark](https://mlsanity.com/paper/2609.29479/) (2026)
- [Hidden Data Challenges in Clinical Machine Learning](https://mlsanity.com/paper/2610.13350/) (2026)

## Abstract (arXiv)

Continuity of care is crucial to ensuring positive health outcomes for patients discharged from an inpatient hospital setting, and improved information sharing can help. To share information, caregivers write discharge notes containing action items to share with patients and their future caregivers, but these action items are easily lost due to the lengthiness of the documents. In this work, we describe our creation of a dataset of clinical action items annotated over MIMIC-III, the largest publicly available dataset of real clinical notes. This dataset, which we call CLIP, is annotated by physicians and covers 718 documents representing 100K sentences. We describe the task of extracting the action items from these documents as multi-aspect extractive summarization, with each aspect representing a type of action to be taken. We evaluate several machine learning models on this task, and show that the best models exploit in-domain language model pre-training on 59K unannotated documents, and incorporate context from neighboring sentences. We also propose an approach to pre-training data selection that allows us to explore the trade-off between size and domain-specificity of pre-training datasets for this task.