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

TL;DR

TracSum is a new benchmark for medical aspect-based summarization that emphasizes sentence-level traceability, enabling users to verify summaries by linking them back to original evidence, and includes a novel evaluation framework.

Contribution

The paper introduces TracSum, a benchmark with annotated medical abstracts, a fine-grained evaluation framework, and a baseline summarization pipeline for traceable, aspect-based summaries.

Findings

Traceability improves summary accuracy and trustworthiness.

Explicit sentence-level tracking enhances generation precision.

Full context incorporation boosts summary completeness.

Abstract

While document summarization with LLMs has enhanced access to textual information, concerns about the factual accuracy of these summaries persist, especially in the medical domain. Tracing evidence 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-citation 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 completeness.