A Survey on Advances in Arabic Long-Text Summarization Strategies | ||
| النشرة المعلوماتية في الحاسبات والمعلومات | ||
| Volume 6, Issue 2 - Serial Number 20240602, July 2024, Pages 1-15 PDF (743.97 K) | ||
| Document Type: المقالة الأصلية | ||
| DOI: 10.21608/fcihib.2024.258854.1103 | ||
| Authors | ||
| mostafa Magdy* 1; سلوى أسامة1; Ensaf Hussein Mohamed1, 2 | ||
| 1Computer Science, Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, Egypt. | ||
| 2School of Information Technology and Computer Science (ITCS), Nile University, Giza, Egypt | ||
| Abstract | ||
| The number of documents, textbooks, and articles is growing exponentially. Thus, the text summarization process aids in recalling the preceding part of a novel before reading the subsequent section. It also facilitates time-saving by allowing readers to peruse summarized versions of lengthy articles or books. This survey aims to present recently published studies on Arabic long-text summarization. Text summarization poses a significant challenge within the domain of Natural Language Processing (NLP). Constructing an effective summary requires accurate text analysis, encompassing complex tasks such as semantic and lexical analysis. Moreover, a quality summary should encapsulate vital details while maintaining conciseness, and it must also consider factors like non-redundancy, relevance, coverage, coherence, and readability. In academic research, various approaches to text summarization are employed, including extractive summarization, abstractive summarization, and hybrid methods. Extractive summarization has reached a level of maturity, leading to a shift in research emphasis towards abstractive summarization and the development of real-time summarization techniques. According to this survey, we found that the abstractive approach is recently used but has many limitations, such as summarizing long text and allowing the user to determine the compression ratio for summarizing the original text. Therefore, the hybrid approach is recommended. | ||
| Keywords | ||
| NLP; Long-text; text summarization; Abstractive Summary; Extractive Summary; hybrid summarization | ||
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