Feature Based Automatic Text Summarization Methods: A Comprehensive State-of-the-Art Survey Articles uri icon

publication date

  • December 2022

start page

  • 133981

end page

  • 134003


  • 10

International Standard Serial Number (ISSN)

  • 2169-3536

Electronic International Standard Serial Number (EISSN)

  • 2169-3536


  • With the advent of the World Wide Web, there are numerous online platforms that generate huge amounts of textual material, including social networks, online blogs, magazines, etc. This textual content contains useful information that can be used to advance humanity. Text summarization has been a significant area of research in natural language processing (NLP). With the expansion of the internet, the amount of data in the world has exploded. Large volumes of data make locating the required and best information time-consuming. It is impractical to manually summarize petabytes of data; hence, computerized text summarization is rising in popularity. This study presents a comprehensive overview of the current status of text summarizing approaches, techniques, standard datasets, assessment criteria, and future research directions. The summarizing approaches are assessed based on several characteristics, including approach-based, document-number-based, Summarization domain-based, document-language-based, output summary nature, etc. This study concludes with a discussion of many obstacles and research opportunities linked to text summarizing research that may be relevant for future researchers in this field.


  • Computer Science


  • abstractive summarization; cosine-similarity; deep learning; extractive summarization; graph-based algorithm; neural networks