Summary of Ontology-constrained Generation Of Domain-specific Clinical Summaries, by Gaya Mehenni and Amal Zouaq
Ontology-Constrained Generation of Domain-Specific Clinical Summaries
by Gaya Mehenni, Amal Zouaq
First submitted to arxiv on: 23 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed novel approach leverages ontologies to create domain-adapted summaries for large language models (LLMs) in text summarization, addressing the open challenge of generating these summaries. The method employs an ontology-guided constrained decoding process to reduce hallucinations while improving relevance, with potential applications in medical domains such as summarizing Electronic Health Records (EHRs). Evaluation on the MIMIC-III dataset demonstrates improvements in generating domain-adapted summaries and reducing hallucination rates. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study solves two big problems in language models. First, it helps create summaries that are tailored to specific topics or areas of expertise, like medicine. This is important because doctors need to quickly find the most relevant information in their patients’ records. Second, the approach reduces “hallucinations” – where AI-generated content includes false information. The researchers developed a new way to use ontologies (like maps for understanding complex data) and decoding processes to make summaries more accurate and relevant. |
Keywords
» Artificial intelligence » Hallucination » Summarization
