Automatic Related Work Section in Scientific Article: Research Trends and Future Directions

• Published in: 2022 International Seminar on Intelligent Technology and Its Applications (ISITIA) pp. 108 - 114
• Main Authors: Justitia, Army, Wang, Hei-Chia
• Format: Conference Proceeding
• Language: English
• Published: IEEE 20-07-2022
• Subjects: automatic related work; Deep learning; deep learning method; Internet; Learning systems; Measurement; scientific article; Seminars; text summarization; Writing
• DOI: 10.1109/ISITIA56226.2022.9855331

Related work plays an important role in the scientific article to track the history of topic evolution and demonstrate the improvement of the proposed method. Writing related work tasks could be arduous for young researchers and scholars. Some studies have attempted to design systems to ease this work besides improving the quality of related work produced. Initially, the research focused on extractive approaches by exploiting the heuristic method, machine learning, or graph-based methods. Recently, deep learning and its variants have become more popular because they can handle abstractive approaches. We propose five research questions that direct discussion and analysis of research in related work summarization. We look for answers to this research question in scientific articles on ScienceDirect, Web of Science, or the Google Scholar Database. This research presents a widespread survey about the trends in related work tasks: related work structure, important aspects, scientific corpora standards, prior proposed methods, baseline systems comparison, evaluation metrics, and challenges in the future. The results of this study provide an overview of research trends in related work summarization, as well as describe research gaps and future research that can still be developed.