Unstructured clinical data can serve as a unique and rich source of information that can meaningfully inform clinical practice. Extracting the most pertinent context from such data is critical for exploiting its true potential toward optimal and timely decision-making in patient care. While prior research has explored various methods for clinical text summarization, most prior studies either process all input tokens uniformly or rely on heuristic-based filters, which can overlook nuanced clinical cues and fail to prioritize information critical for decision-making. In this study, we propose Contextual, a novel framework that integrates a Context-Preserving Token Filtering method with a Domain-Specific Knowledge Graph (KG) for contextual augmentation. By preserving context-specific important tokens and enriching them with structured knowledge, ConTextual improves both linguistic coherence and clinical fidelity. Our extensive empirical evaluations on two public benchmark datasets demonstrate that ConTextual consistently outperforms other baselines. Our proposed approach highlights the complementary role of token-level filtering and structured retrieval in enhancing both linguistic and clinical integrity, as well as offering a scalable solution for improving precision in clinical text generation.

本研究解决了如何从无结构临床数据中提取相关上下文的问题，传统方法往往忽视重要信息。提出的ConTextual框架结合了上下文保持的令牌过滤方法和领域特定的知识图谱，显著提高了临床文本生成的语言连贯性和临床真实性。实证评估表明，该方法在多个基准数据集上优于其他模型，展示了令牌过滤和结构化检索的互补作用。

ConTextual：通过保持上下文的令牌过滤和知识图谱改善临床文本摘要