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.

Questo articolo esplora i giri e le loro implicazioni.

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