---
title: "Response Content Units — paper by Park et al. (2026)"
description: "The authors propose the medical Response Content Units (RCU) schema to address this. 13 references."
authors: ["Yongsin Park", "Wen-wai Yim", "Emma McKibbin", "Asma Ben Abacha", "Fei Xia"]
year: 2026
venue: "Proceedings of the Fifth Workshop on Generation, Evaluation and Metrics (GEM)"
source: "https://aclanthology.org/2026.gem-main.25/"
doi: "10.18653/v1/2026.gem-main.25"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/park-2026-response-content-units-evaluating-completeness/"
---
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# Response Content Units: Evaluating Completeness and Proactiveness in Medical Open-Response Question Answering

**Authors:** Yongsin Park, [Wen-wai Yim](https://md.research.elamur.ai/free/en/authors/wen-wai-yim/), Emma McKibbin, [Asma Ben Abacha](https://md.research.elamur.ai/free/en/authors/asma-ben-abacha/) and [Fei Xia](https://md.research.elamur.ai/free/en/authors/fei-xia/) · **Year:** 2026 · **Venue:** [Proceedings of the Fifth Workshop on Generation, Evaluation and Metrics (GEM)](https://md.research.elamur.ai/free/en/venues/gem/) ([GEM 2026](https://md.research.elamur.ai/free/en/venues/gem/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.gem-main.25/) · [PDF](https://aclanthology.org/2026.gem-main.25.pdf) · [DOI 10.18653/v1/2026.gem-main.25](https://doi.org/10.18653/v1/2026.gem-main.25) · [OpenAlex](https://openalex.org/W7166824839)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Patient messages sent through remote-care portals are often messy. They may bundle several concerns, contain no explicit question at all, or call for clinical advice the patient never asked for. Standard QA benchmarks use clean one-question-one-answer pairs, and reference-based metrics tend to penalize "extra" content, so they reward neither complete coverage nor proactive guidance. The authors propose the medical Response Content Units (RCU) schema to address this. It splits queries into sub-sentence questions with unique IDs, links questions that mean the same thing, and maps each response sentence either to the question it answers or to a proactive label (medical directive or prognosis), with extra attributes. They manually annotated text from two existing visual QA datasets, producing DermaVQA-RCU and WoundcareVQA-RCU. Their analysis shows that professionals leave many explicit questions unanswered. It also shows that directives appear far more often than direct answers. LLM baselines for the extraction and classification tasks performed well, and the authors see this as support for clinician-assistive tools that flag unaddressed concerns.

## Key points
- **Dataset sizes:**
  - DermaVQA-RCU has 306 inquiries, averaging 2.18 questions each. It is built from the IIYI threads, translated from Chinese.
  - WoundcareVQA-RCU has 476 inquiries, averaging 1.82 questions each. It is built from Baidu Tieba and Baidu Zhidao posts, with answers written by U.S. physicians.
- **Unanswered questions:** professional (Gold) responses left 16.4% of unique explicit questions unanswered.
- **Proactive content:** medical directives occur 2.4 to 12.1 times as often as direct answers.
- **Annotation attributes:**
  - Questions carry polarity (open/binary), a type (e.g., identification, advice, outcome) and an answer value.
  - Directives carry a category plus `is_severe` and `is_conditional` flags.
- **Release and limitations:** the annotations and guidelines are on OSF, and the code is on GitHub (RCU-MORQA). Coverage is limited to dermatology and wound care, and the baseline prompts were not tuned for each model.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Yongsin Park, Wen-wai Yim, Emma McKibbin, Asma Ben Abacha and Fei Xia. 2026. Response Content Units: Evaluating Completeness and Proactiveness in Medical Open-Response Question Answering. Proceedings of the Fifth Workshop on Generation, Evaluation and Metrics (GEM).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{park2026response,
  title = {{Response Content Units: Evaluating Completeness and Proactiveness in Medical Open-Response Question Answering}},
  author = {Yongsin Park, Wen-wai Yim, Emma McKibbin, Asma Ben Abacha and Fei Xia},
  booktitle = {Proceedings of the Fifth Workshop on Generation, Evaluation and Metrics (GEM)},
  year = {2026},
  doi = {10.18653/v1/2026.gem-main.25},
  url = {https://doi.org/10.18653/v1/2026.gem-main.25}
}
```

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## References

13 references, 9 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 1 November 2026*