# MAPLE: Medical Aspect-Based Summarization with Phrase-Level Evidence

arXiv: https://arxiv.org/abs/2601.03418 · HTML: https://mlsanity.com/paper/2601.03418/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 34 references that are in the map.
- First posted on arXiv 6 Jan 2026.

## Builds on (most cited first, 17 of 17)

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- [When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories](https://mlsanity.com/paper/2212.10511/) (2022)
- [Enabling Large Language Models to Generate Text with Citations](https://mlsanity.com/paper/2305.14627/) (2023)
- [Asking and Answering Questions to Evaluate the Factual Consistency of Summaries](https://mlsanity.com/paper/2004.04228/) (2020)
- [Teaching language models to support answers with verified quotes](https://mlsanity.com/paper/2203.11147/) (2022)
- [Understanding Neural Networks through Representation Erasure](https://mlsanity.com/paper/1612.08220/) (2016)
- [Re-evaluating Evaluation in Text Summarization](https://mlsanity.com/paper/2010.07100/) (2020)
- [WikiAsp: A Dataset for Multi-domain Aspect-based Summarization](https://mlsanity.com/paper/2011.07832/) (2020)
- [Attribute First, then Generate: Locally-attributable Grounded Text Generation](https://mlsanity.com/paper/2403.17104/) (2024)
- [OASum: Large-Scale Open Domain Aspect-based Summarization](https://mlsanity.com/paper/2212.09233/) (2022)
- [ACLSum: A New Dataset for Aspect-based Summarization of Scientific Publications](https://mlsanity.com/paper/2403.05303/) (2024)
- [FactPICO: Factuality Evaluation for Plain Language Summarization of Medical Evidence](https://mlsanity.com/paper/2402.11456/) (2024)
- [DocLens: Multi-aspect Fine-grained Evaluation for Medical Text Generation](https://mlsanity.com/paper/2311.09581/) (2023)
- [MACSum: Controllable Summarization with Mixed Attributes](https://mlsanity.com/paper/2211.05041/) (2022)
- [TracSum: A New Benchmark for Aspect-Based Summarization with Sentence-Level Traceability in Medical Domain](https://mlsanity.com/paper/2508.13798/) (2025)

## Abstract (arXiv)

Trustworthy clinical summarization requires every claim to be traceable to its evidence, yet existing attribution often resolves only to the sentence or document, leaving clinicians to scan surrounding text for the few words that matter. We argue that the unit of attribution should match the unit of verification: the precise phrase the reader's eye must land on. We present MAPLE (Medical Aspect-Based Summarization with Phrase-Level Evidence), a human-annotated benchmark that grounds each summarized claim in both cited sentences and contributory phrases within them. Spanning 152 randomized controlled trial (RCT) abstracts and 16 clinically motivated aspects, MAPLE comprises 1,799 aspect-based summaries with two-level evidence. We further introduce a decoupled evaluation framework that separately scores content, traceability, and locatability, together with a proxy for the amount of source text a clinician must inspect to verify a claim. Benchmarking eleven LLMs shows that sentence-level citation is consistently strong (C-F1 up to 90.9%), while phrase-level grounding remains less stable and the most discriminative axis across models (P-F1 66.1-84.5%). These results suggest that the key challenge is not only producing accurate summaries, but localizing their supporting evidence precisely enough for efficient clinical verification. Data and code are available at https://github.com/chubohao/maple.