Structured abstract generator (SAG) model: analysis of IMRAD structure of articles and its effect on extractive summarization

Abstract

An abstract is the most crucial element that may convince readers to read the complete text of a scientific publication. However, studies show that in terms of organization, readability, and style, abstracts are also among the most troublesome parts of the pertinent manuscript. The ultimate goal of this article is to produce better understandable abstracts with automatic methods that will contribute to scientific communication in Turkish. We propose a summarization system based on extractive techniques combining general features that have been shown to be beneficial for Turkish. To construct the data set for this aim, a sample of 421 peer-reviewed Turkish articles in the field of librarianship and information science was developed. First, the structure of the full-texts, and their readability in comparison with author abstracts, were examined for text quality evaluation. A content-based evaluation of the system outputs was then carried out. System outputs, in cases of using and ignoring structural features of full-texts, were compared. Structured outputs outperformed classical outputs in terms of content and text quality. Each output group has better readability levels than their original abstracts. Additionally, it was discovered that higher-quality outputs are correlated with more structured full-texts, highlighting the importance of structural writing. Finally, it was determined that our system can facilitate the scholarly communication process as an auxiliary tool for authors and editors. Findings also indicate the significance of structural writing for better scholarly communication.