What is it about?
Abstractive text summarization is an advanced artificial intelligence technique that automatically creates summaries of documents in a way that sounds like human writing. This systematic review examines 226 research studies published between 2011 and 2023, exploring how computers can generate concise, meaningful summaries without simply copying text. The research investigates the challenges, methods, datasets, and evaluation techniques used in creating these AI-powered summaries.
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Why is it important?
This study is crucial because it provides a comprehensive roadmap for researchers working on AI-powered text summarization. By analyzing over 200 studies, the research identifies key challenges and opportunities in developing more intelligent summarization technologies. The proposed conceptual framework and guidelines can help researchers and developers create more accurate and human-like summary generation systems, which could revolutionize how we process and understand large volumes of text across various fields like journalism, research, and information management.
Perspectives
Our journey through the peer review process for this systematic literature review was transformative. Responding to the reviewers' comments not only refined our paper but also fundamentally reshaped our approach to research in artificial intelligence. This experience taught us the importance of rigorous analysis, clear communication, and the value of constructive feedback. We learned that a high-quality research paper is not just about the findings, but about how those findings are thoughtfully organized, presented, and contextualized. The process of revision became a learning experience that went far beyond this single paper, providing us with invaluable insights into effective academic writing and research methodology in the rapidly evolving field of AI text summarization.
Dr. Sanjay Singh
Manipal Institute of Technology, Manipal
Read the Original
This page is a summary of: Single-Document Abstractive Text Summarization: A Systematic Literature Review, ACM Computing Surveys, October 2024, ACM (Association for Computing Machinery),
DOI: 10.1145/3700639.
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