In this study, presented a comprehensive evaluation of abstractive and extractive summarization performance across three prominent large language models (LLMs): ChatGPT, DeepSeek, and Gemini. A total of 8,000 cardiovascular-related research abstracts were collected from PubMed and summarized using two distinct promptin...
Burcu Baştürk, Aytuğ Onan· Sakarya University Journal o...· 0 citations
The results indicate that abstractive summaries, particularly those generated by Gemini and ChatGPT, achieve coherence levels comparable to or higher than original abstracts under LDA and LSA, while extractive summaries exhibit greater variability.
: 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...
Findings demonstrate that compact models can achieve strong biomedical classification performance through KD under compatible teacher–student pairings, while also highlighting that KD effectiveness varies substantially depending on the specific model combination.
