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 source 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 1 for seven key medical aspects, yielding 3.5K summarycitations 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, aspectbased 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.

Scene text recognition has become a hot topic in recent years due to its booming real-life applications. Attention-based encoder-decoder framework has become one of the most popular frameworks especially in the irregular text scenario. However, the “attention drift” problem hinders the recognition performance for most existing attention-based scene text recognition methods. To solve this problem, we propose an auxiliary supervision branch along with the attention-based encoder-decoder framework. A new loss function is designed to refine the feature map and to help the attention region align the target character area. Compared with existing attention rectification mechanisms, our method does not require character-level annotations or introduce any additional trainable parameter. Furthermore, our method can improve the performance for both RNN-Attention and Scaled Dot-Product Attention. The experiment results on various benchmarks have demonstrated that the proposed approach outperforms the state-of-the-art methods in both regular and irregular text recognition scenarios.