A Comparative Genre Analysis Study of Scientific Articles Abstracts and AI-Generated Abstracts
Date
2024-06-18
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
ORCID
0000-0002-9767-6175
Type
Thesis
Degree Level
Masters
Abstract
This study investigated and compared the rhetorical moves and steps employed in human-written abstracts of published articles with those generated by ChatGPT, 3.5 free version. The grammatical features and move patterns are also analyzed. The data for this study was collected by compiling corpus of 25 original research papers’ abstracts and 25 ChatGPT-generated abstracts. The ChatGPT abstracts were based on the titles of the 25 original abstracts collected prior, which were all published in Q1 journals from 12 different disciplines. Those disciplines are Psychology, Economics, Biology, Physics, Geology, Artificial Intelligence, Linguistics, Sociology, Agriculture, Mechanical Engineering, Management, and Sports Medicine. UAM CorpusTool (version 2.8), a corpus annotation tool, was used to annotate the texts based on a scheme developed by the researcher. Adapted from the IMRD (Introduction-Methods-Results-Discussion model for abstract writing, this scheme contains four moves, namely, Introduction-Methods-Results-Conclusion or IMRC, and a total of 21 steps. This adaptation reflected the rhetorical structures identified in the corpus. Four moves (Introduction, Methodology, Results, and Conclusion) were found to be employed in the abstracts written by human authors and those generated by ChatGPT. It was found that the Results move is the most frequent move in ChatGPT abstracts, while the Introduction move is the most frequent one in original abstracts. Significant differences were found in the frequency of the Introduction and Conclusion moves, and key literature findings step between the ChatGPT-generated abstracts and original abstracts. Such differences are likely due to the nature of the training data used for the AI model. As for the linguistic features, a high tendency was found in both ChatGPT and original abstracts to use past tense and simple present tense, content words, and active voice. However, significant differences found between ChatGPT and original abstracts indicated that simple present was employed much more in ChatGPT abstracts than in original abstracts, while the opposite was the case for simple past tense. Significant differences in the use of pronouns also showed that this part of speech is used significantly more in original abstracts. The move sequence patterns found showed that the patterns of ChatGPT is similar to that of typical research publications, while human abstracts display a variety of move sequence patterns. The findings of the study show that ChatGPT abstracts mimic original abstracts in terms of the rhetorical move pattern and in most of the steps used in each move. The researcher concludes the study with recommendations for future researchers based on the findings of the current study.
Description
Keywords
ChatGPT, Move Analysis
Citation
Degree
Master of Arts (M.A.)
Department
Linguistics
Program
Linguistics