TracSum: A New Benchmark for Aspect-Based Summarization with Sentence-Level Traceability in Medical Domain

ACL ARR 2025 May Submission2228 Authors

Abstract: While document summarization with LLMs has enhanced access to textual information, concerns about the factual accuracy of these summaries persist (e.g., hallucination), especially in the medical domain. Identifying citations from which summaries are derived enables users to assess their accuracy, thereby alleviating this concern. In this paper, we introduce TracSum, a novel benchmark for traceable, aspect-based summarization, in which generated summaries are paired with sentence-level citations, enabling users to trace back to the original context. First, we annotate 500 medical abstracts for seven key medical aspects, yielding 3.5K summary-citations pairs. We then propose a fine-grained evaluation framework for this new task, designed to assess the completeness and consistency of generated content using four metrics. Finally, we introduce a summarization pipeline, Track-Then-Sum, which serves as a baseline method for comparison. In experiments, we evaluate both this baseline and a set of LLMs on TracSum, and conduct a human evaluation to assess the evaluation results. The findings demonstrate that TracSum can serve as an effective benchmark for traceable, aspect-based summarization tasks. We also observe that explicitly performing sentence-level tracking prior to summarization enhances generation accuracy, while incorporating the full context further improves summary completeness. The visualized dataset is anonymously available at https://www.tracsum.info/.

Paper Type: Long

Research Area: Summarization

Research Area Keywords: Summarization,Resources and Evaluation,Generation,Language Modeling

Contribution Types: NLP engineering experiment, Data resources

Languages Studied: English

Keywords: Summarization, Resources and Evaluation, Generation, Language Modeling

Submission Number: 2228