ConTextual: Improving Clinical Text Summarization in LLMs with Context-preserving Token Filtering and Knowledge Graphs (2504.16394v2)

Abstract: 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.

Improving Clinical Text Summarization with Context Enrichment: A Review of "ConTextual"

The paper "ConTextual: Improving Clinical Text Summarization in LLMs with Context-preserving Token Filtering and Knowledge Graphs," authored by Fahmida Liza Piya and Rahmatollah Beheshti, addresses critical challenges in processing unstructured clinical data to enhance clinical decision-making efficiency. With the exponential growth in unstructured data, particularly within Electronic Health Records (EHRs), extracting pertinent information that maintains clinical relevance becomes imperative. The significance of this work lies in proposing a method to mitigate the inherent verbosity and redundancy of clinical narratives, thus improving summarization precision and efficiency.

Core Contributions and Methodology

The authors introduce ConTextual, a novel approach that synergistically integrates Context-Preserving Token Filtering (CPTF) and Domain-Specific Knowledge Graphs (KGs). The framework's design aims to resolve the challenges associated with summarizing verbose and complex clinical narratives—specifically, issues related to coherence and factual fidelity often observed in state-of-the-art LLMs.

1. Context-Preserving Token Filtering (CPTF):

ConTextual utilizes CPTF to dynamically identify and selectively retain tokens of high semantic significance while discarding redundant ones. This token filtering is achieved through the attention mechanisms native to transformer models, which assess token importance and reduce irrelevant details efficiently. This method balances computational costs with information retention, ensuring succinct narratives pivotal for clinical assessments.

2. Domain-Specific Knowledge Graph Integration:

To counteract the potential loss of information due to token filtering, the authors have constructed a domain-specific KG. This KG enriches filtered tokens with structured clinical entity relationships, such as diagnoses and treatments. By embedding these relationships, ConTextual enhances the contextual fidelity required in complex medical scenarios.

3. Retrieval-Augmented Generation (RAG):

Through RAG, the framework integrates dynamically retrieved KG context with LLM generation processes to maintain accuracy. This retrieval process uses a function that maps clinical tokens to structures within the KG, providing adaptive augmentation tailored to specific clinical notes.

Experimental Evaluation

The developers employ empirical evaluations on clinical discharge summaries using the MIMIC-IV dataset, demonstrating that ConTextual improves both ROUGE-L and BLEU-1 scores significantly compared to baseline models. Results show a 50% improvement in ROUGE-L and a 20% enhancement in BLEU-1 metrics, indicating superior linguistic coherence and clinical fidelity. Moreover, efficiency metrics reveal scalability benefits in terms of reduced latency and increased throughput—an essential consideration for real-world application in healthcare environments where computational resources may be limited.

Implications and Future Directions

ConTextual's modular framework extends beyond healthcare, having potential applications in other domains such as legal documentation and scientific literature analysis. By effectively managing verbose narratives while integrating domain-specific knowledge, ConTextual provides a scalable solution to summarization tasks requiring precision and domain awareness.

Looking forward, the approach opens avenues for further research in integrating multi-modal data, advancing model architectures to improve token selection dynamically, and enhancing real-time processing capabilities. Practically, enhancing clinical AI systems with such enriched summarization models can lead to better-informed healthcare decisions, streamlined clinical workflows, and reduced documentation burdens.

This research contributes significantly to the domain of medical NLP by proposing mechanisms to elevate clinical text processing. The integration of domain knowledge with LLM capabilities exemplifies a promising direction for addressing summarization challenges in medical informatics, ultimately harnessing AI for improved patient care outcomes.