The University of Amsterdam’s participation in the TREC 2024 Plain Language Adaptation of Biomedical Abstracts (PLABA) Track is documented and the effectiveness of text simplification models trained on aligned pairs of sentences in biomedical abstracts and plain language summaries is investigated.
: Biomedical texts naturally contain multiple biological and medical concepts within a document, resulting in a semantically rich and complex structure. Consequently, multi-label text classification (MLTC) has become a suitable framework for comprehensively modeling biomedical texts, including clinical reports, laborat...
In this study, a comprehensive evaluation of abstractive and extractive summarization performance across three prominent large language models (LLMs): ChatGPT, DeepSeek, and Gemini is presented. A total of 8,000 cardiovascular-related research abstracts were collected from PubMed and summarized using two distinct promp...
Burcu Baştürk, Aytuğ Onan· Sakarya University Journal o...· 0 citations
These findings provide a systematic reference for selecting biomedical summarization tools and highlight that broad pretraining outperforms narrow domain adaptation.
Fabio Baumgärtel, Enrico Bono, Lucas Fillinger et al.· iScience· 0 citations
A hybrid extract-then-summarize framework that first identifies salient sentences using a supervised extractive model and then generates an abstractive summary through LLM prompting is proposed, which improves efficiency by focusing the generative process on informative content while reducing the processing cost typica...
Azzedine Aftiss, Salima Lamsiyah, Christoph Schommer et al.· IEEE Access· 0 citations
A benchmark-guided, scalable framework for automated medical terminology standardization that accepts heterogeneous short medical expressions without manual input pre-processing and automatically performs text refinement, semantic retrieval and terminology mapping to standardized concepts and vocabulary codes is establ...
Anshul Verma, Abhijay, Manan Vangani et al.· bioRxiv· 0 citations
The results show that BART achieves the best performance with an ROUGE-2 F1-score of 0.40664, while T5 demonstrates superior grammatical acceptability, achieving 93.36%, but BART achieves a very near performance to T5.
Emad Nabil· Islamic University Journal o...· 0 citations
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