MedConclusion: A Benchmark for Biomedical Conclusion Generation from Structured Abstracts

• URL: http://arxiv.org/abs/2604.06505v1
• Date: Tue, 07 Apr 2026 22:34:02 GMT
• Title: MedConclusion: A Benchmark for Biomedical Conclusion Generation from Structured Abstracts
• Abstract summary: MedConclusion is a large-scale dataset of structured abstracts for biomedical conclusion generation.<n>Each instance pairs the non-conclusion sections of an abstract with the original author-written conclusion.<n>MedConclusion also includes journal-level metadata such as biomedical category and SJR.
• Score: 6.305549902494417
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) are widely explored for reasoning-intensive research tasks, yet resources for testing whether they can infer scientific conclusions from structured biomedical evidence remain limited. We introduce $\textbf{MedConclusion}$, a large-scale dataset of $\textbf{5.7M}$ PubMed structured abstracts for biomedical conclusion generation. Each instance pairs the non-conclusion sections of an abstract with the original author-written conclusion, providing naturally occurring supervision for evidence-to-conclusion reasoning. MedConclusion also includes journal-level metadata such as biomedical category and SJR, enabling subgroup analysis across biomedical domains. As an initial study, we evaluate diverse LLMs under conclusion and summary prompting settings and score outputs with both reference-based metrics and LLM-as-a-judge. We find that conclusion writing is behaviorally distinct from summary writing, strong models remain closely clustered under current automatic metrics, and judge identity can substantially shift absolute scores. MedConclusion provides a reusable data resource for studying scientific evidence-to-conclusion reasoning. Our code and data are available at: https://github.com/Harvard-AI-and-Robotics-Lab/MedConclusion.

Related papers

• Divide-Prompt-Refine: a Training-Free, Structure-Aware Framework for Biomedical Abstract Generation [0.8774270519266251]
  We propose DPR-BAG (Divide, Prompt, and Refine for Biomedical Abstract Generation) to generate coherent and factually grounded abstracts for biomedical articles with full text but no abstract.<n>DPR-BAG decomposes full-text documents into structured rhetorical facets following the Background-Objective-Methods-Results-Conclusions (BOMRC) schema.<n>It improves abstractive novelty over strong extractive and fine-tuned baselines, while maintaining factual consistency.
  arXiv  Detail & Related papers  (2026-05-20T02:25:21Z)
• PaperMind: Benchmarking Agentic Reasoning and Critique over Scientific Papers in Multimodal LLMs [68.27437550335709]
  We introduce PaperMind, a benchmark designed to evaluate integrated and agent-oriented scientific reasoning over research papers.<n>PaperMind is constructed from real scientific papers across seven domains, including agriculture, biology, chemistry, computer science, medicine, physics, and economics.<n>By analyzing model behavior across multiple tasks, PaperMind enables a diagnostic evaluation of integrated scientific reasoning behaviors.
  arXiv  Detail & Related papers  (2026-04-23T05:42:39Z)
• EvidenceOutcomes: a Dataset of Clinical Trial Publications with Clinically Meaningful Outcomes [17.22091807858547]
  EvidenceOutcomes is a novel, large, annotated corpus of clinically meaningful outcomes extracted from biomedical literature.<n>EvidenceOutcomes can serve as a shared benchmark to develop and test future machine learning algorithms.
  arXiv  Detail & Related papers  (2025-06-03T02:22:06Z)
• Advancing AI Research Assistants with Expert-Involved Learning [84.30323604785646]
  Large language models (LLMs) and large multimodal models (LMMs) promise to accelerate biomedical discovery, yet their reliability remains unclear.<n>We introduce ARIEL (AI Research Assistant for Expert-in-the-Loop Learning), an open-source evaluation and optimization framework.<n>We find that state-of-the-art models generate fluent but incomplete summaries, whereas LMMs struggle with detailed visual reasoning.
  arXiv  Detail & Related papers  (2025-05-03T14:21:48Z)
• Causal Representation Learning from Multimodal Biomedical Observations [57.00712157758845]
  We develop flexible identification conditions for multimodal data and principled methods to facilitate the understanding of biomedical datasets.<n>Key theoretical contribution is the structural sparsity of causal connections between modalities.<n>Results on a real-world human phenotype dataset are consistent with established biomedical research.
  arXiv  Detail & Related papers  (2024-11-10T16:40:27Z)
• NeuroSym-BioCAT: Leveraging Neuro-Symbolic Methods for Biomedical Scholarly Document Categorization and Question Answering [0.14999444543328289]
  We introduce a novel approach that integrates an optimized topic modelling framework, OVB-LDA, with the BI-POP CMA-ES optimization technique for enhanced scholarly document abstract categorization. We employ the distilled MiniLM model, fine-tuned on domain-specific data, for high-precision answer extraction.
  arXiv  Detail & Related papers  (2024-10-29T14:45:12Z)
• FactPICO: Factuality Evaluation for Plain Language Summarization of Medical Evidence [46.71469172542448]
  This paper presents FactPICO, a factuality benchmark for plain language summarization of medical texts. It consists of 345 plain language summaries of abstracts generated from three randomized controlled trials (RCTs) We assess the factuality of critical elements of RCTs in those summaries, as well as the reported findings concerning these.
  arXiv  Detail & Related papers  (2024-02-18T04:45:01Z)
• High-throughput Biomedical Relation Extraction for Semi-Structured Web Articles Empowered by Large Language Models [1.9665865095034865]
  We formulate the relation extraction task as binary classifications for large language models. We designate the main title as the tail entity and explicitly incorporate it into the context. Longer contents are sliced into text chunks, embedded, and retrieved with additional embedding models.
  arXiv  Detail & Related papers  (2023-12-13T16:43:41Z)
• EBOCA: Evidences for BiOmedical Concepts Association Ontology [55.41644538483948]
  This paper proposes EBOCA, an ontology that describes (i) biomedical domain concepts and associations between them, and (ii) evidences supporting these associations. Test data coming from a subset of DISNET and automatic association extractions from texts has been transformed to create a Knowledge Graph that can be used in real scenarios.
  arXiv  Detail & Related papers  (2022-08-01T18:47:03Z)
• CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark [51.38557174322772]
  We present the first Chinese Biomedical Language Understanding Evaluation benchmark. It is a collection of natural language understanding tasks including named entity recognition, information extraction, clinical diagnosis normalization, single-sentence/sentence-pair classification. We report empirical results with the current 11 pre-trained Chinese models, and experimental results show that state-of-the-art neural models perform by far worse than the human ceiling.
  arXiv  Detail & Related papers  (2021-06-15T12:25:30Z)
• MS2: Multi-Document Summarization of Medical Studies [11.38740406132287]
  We release MS2 (Multi-Document Summarization of Medical Studies), a dataset of over 470k documents and 20k summaries derived from the scientific literature. This dataset facilitates the development of systems that can assess and aggregate contradictory evidence across multiple studies. We experiment with a summarization system based on BART, with promising early results.
  arXiv  Detail & Related papers  (2021-04-13T19:59:34Z)
• Modeling Shared Responses in Neuroimaging Studies through MultiView ICA [94.31804763196116]
  Group studies involving large cohorts of subjects are important to draw general conclusions about brain functional organization. We propose a novel MultiView Independent Component Analysis model for group studies, where data from each subject are modeled as a linear combination of shared independent sources plus noise. We demonstrate the usefulness of our approach first on fMRI data, where our model demonstrates improved sensitivity in identifying common sources among subjects.
  arXiv  Detail & Related papers  (2020-06-11T17:29:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.