Shared Task at Biolaysumm2025 : Extract then summarize approach Augmented with UMLS based Definition Retrieval for Lay Summary generation.

We present LayForge, a two-track lay summary generation system developed for the Bio-LaySumm2025 shared task (Xiao et al., 2025) .Task 1.1 addresses the lay summarization using only the internal content of the article, while Task 1.2 augments this process with domain knowledge such as biomedical definitions and concept explanations.BioLaySumm employs a modular architecture that leverages large language models (LLMs), a BioBERTbased named entity recognizer (NER), and the UMLS (Bodenreider, 2004) knowledge base to create readable, informative, and faithful lay summaries.Our system shows strong performance on both tasks when evaluated on the PLOS and elife subset (Goldsack et al., 2022), particularly in readability and factuality metrics.The architecture illustrates how modularity and domain adaptation can be effectively combined for accessible biomedical communication.

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Publication Details

Published
2025-01-01
DOI
https://doi.org/10.18653/v1/2025.bionlp-share.23
Primary Topic
Biomedical Text Mining and Ontologies
Type
article
Field-Weighted Citation Impact
0.00

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article

Shared Task at Biolaysumm2025 : Extract then summarize approach Augmented with UMLS based Definition Retrieval for Lay Summary generation.

Parameswari Krishnamurthy, Anubha Gupta
Biomedical Text Mining and Ontologies
article

Shared Task at Biolaysumm2025 : Extract then summarize approach Augmented with UMLS based Definition Retrieval for Lay Summary generation.

Parameswari Krishnamurthy, Anubha Gupta
article en

Abstract

We present LayForge, a two-track lay summary generation system developed for the Bio-LaySumm2025 shared task (Xiao et al., 2025) .Task 1.1 addresses the lay summarization using only the internal content of the article, while Task 1.2 augments this process with domain knowledge such as biomedical definitions and concept explanations.BioLaySumm employs a modular architecture that leverages large language models (LLMs), a BioBERTbased named entity recognizer (NER), and the UMLS (Bodenreider, 2004) knowledge base to create readable, informative, and faithful lay summaries.Our system shows strong performance on both tasks when evaluated on the PLOS and elife subset (Goldsack et al., 2022), particularly in readability and factuality metrics.The architecture illustrates how modularity and domain adaptation can be effectively combined for accessible biomedical communication.

International Institute of Information Technology, Hyderabad (IN)
Louisiana Transportation Research Center, International Institute of Information Technology, Hyderabad
Openalex Percentile: Top 83%
Biomedical Text Mining and Ontologies
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