  The pursuit of article or text summarization has captured the attention of
natural language processing (NLP) practitioners, presenting itself as a
formidable challenge. ChatGPT 3.5 exhibits the capacity to condense the content
of up to 3000 tokens into a single page, aiming to retain pivotal information
from a given text across diverse themes. In a conducted qualitative research
endeavor, we selected seven scientific articles and employed the publicly
available ChatGPT service to generate summaries of these articles.
Subsequently, we engaged six co-authors of the articles in a survey, presenting
five questions to evaluate the quality of the summaries compared to the
original content. The findings revealed that the summaries produced by ChatGPT
effectively encapsulated the crucial information present in the articles,
preserving the principal message of each manuscript. Nonetheless, there was a
slight diminishment in the technical depth of the summaries as opposed to the
original articles. As a result, our conclusion underscores ChatGPT's text
summarization capability as a potent tool for extracting essential insights in
a manner more aligned with reporting than purely scientific discourse.
