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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.03418 |
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| _version_ | 1866909983191334912 |
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| author | Chu, Bohao Frihat, Sameh Pakull, Tabea M. G. Damm, Hendrik Li, Meijie Muhabbek, Ula Lodde, Georg Fuhr, Norbert |
| author_facet | Chu, Bohao Frihat, Sameh Pakull, Tabea M. G. Damm, Hendrik Li, Meijie Muhabbek, Ula Lodde, Georg Fuhr, Norbert |
| contents | Verifying system-generated summaries remains challenging, as effective verification requires precise attribution to the source context, which is especially crucial in high-stakes medical domains. To address this challenge, we introduce PCoA, an expert-annotated benchmark for medical aspect-based summarization with phrase-level context attribution. PCoA aligns each aspect-based summary with its supporting contextual sentences and contributory phrases within them. We further propose a fine-grained, decoupled evaluation framework that independently assesses the quality of generated summaries, citations, and contributory phrases. Through extensive experiments, we validate the quality and consistency of the PCoA dataset and benchmark several large language models on the proposed task. Experimental results demonstrate that PCoA provides a reliable benchmark for evaluating system-generated summaries with phrase-level context attribution. Furthermore, comparative experiments show that explicitly identifying relevant sentences and contributory phrases before summarization can improve overall quality. The data and code are available at https://github.com/chubohao/PCoA. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_03418 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PCoA: A New Benchmark for Medical Aspect-Based Summarization With Phrase-Level Context Attribution Chu, Bohao Frihat, Sameh Pakull, Tabea M. G. Damm, Hendrik Li, Meijie Muhabbek, Ula Lodde, Georg Fuhr, Norbert Computation and Language Verifying system-generated summaries remains challenging, as effective verification requires precise attribution to the source context, which is especially crucial in high-stakes medical domains. To address this challenge, we introduce PCoA, an expert-annotated benchmark for medical aspect-based summarization with phrase-level context attribution. PCoA aligns each aspect-based summary with its supporting contextual sentences and contributory phrases within them. We further propose a fine-grained, decoupled evaluation framework that independently assesses the quality of generated summaries, citations, and contributory phrases. Through extensive experiments, we validate the quality and consistency of the PCoA dataset and benchmark several large language models on the proposed task. Experimental results demonstrate that PCoA provides a reliable benchmark for evaluating system-generated summaries with phrase-level context attribution. Furthermore, comparative experiments show that explicitly identifying relevant sentences and contributory phrases before summarization can improve overall quality. The data and code are available at https://github.com/chubohao/PCoA. |
| title | PCoA: A New Benchmark for Medical Aspect-Based Summarization With Phrase-Level Context Attribution |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2601.03418 |