Evaluating the Coherence and Diversity in AI-Generated and Paraphrased Scientific Abstracts: A Fuzzy Topic Modeling Approach
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In an era where Artificial Intelligence (AI) plays a pivotal role in the generation and paraphrasing of scientific literature, understanding its impact on the integrity and coherence of scholarly content is crucial. This study embarks on an exploratory analysis to assess the differences in topic modeling outcomes among three distinct sets of radiology-related abstracts: original scientific abstracts from PubMed, AI-paraphrased abstracts, and AI-generated abstracts. Utilizing advanced fuzzy topic modeling techniques, which excel in handling the inherent ambiguity and nuances in natural language, this research aims to provide a comprehensive analysis of topic interpretability, coherence, and diversity within these datasets. By applying methods such as Fuzzy Latent Semantic Analysis (FLSA) and its variants, FLSA-W and FLSA-V, the study endeavors to unearth the subtle semantic shifts and thematic variances introduced by AI in scientific discourse. The findings are expected to reveal critical insights into how AI transformations influence the thematic fabric of scientific literature, potentially reshaping our understanding of AI's role in scholarly communication. This research not only contributes to the discourse on AI in academic writing but also showcases the effectiveness of fuzzy topic modeling in analyzing complex text corpora, underscoring its significance in the ever-evolving landscape of computational linguistics.