Text summarization based on human behavioural learning model

text summarization methods


  • Namita Kale MET'S Institute of Engineering
  • Ranjana P. Dahake MET'S Institute of Engineering
  • Kalpana V. Metre MET'S Institute of Engineering


Data Science, Big data, automated text summarization, Artificial Intelligence, optimized deep learning methods, computational cognitive models


Summarization of text documents has begun to play an important role in information collection. Summarization has traditionally been done physically by humans, which has resulted in a time-consuming operation as the volume of data has become more and larger. With the goal of resolving this issue, automated text summarization has become a critical need for efficiently managing the congested data. Previous research on text summarization has focused on summarizing pre-specified materials with no extra requirements, and is sometimes referred to as generic summary. Automatic document summarization, on the other hand, is the function of reducing the size of papers while still providing considerable semantic value. The development of recent advances in communication field has brought up deep learning method and human knowledge intervention with cognitive model. As a result, this study investigates how modern artificial intelligence with optimized deep learning methods, as well as human information processing behavior, structures, and underlying processes, might be utilized in document summarization utilizing computational cognitive models. Based on precision, recall, and F-measure, this study also examines the usefulness of these models and their application in diverse document summarizing settings and activities.





How to Cite

Kale, N., Dahake, R. P., & Metre, K. V. (2023). Text summarization based on human behavioural learning model. Journal of Integrated Science and Technology, 12(2), 741. Retrieved from https://pubs.thesciencein.org/journal/index.php/jist/article/view/a741



Computer Sciences and Mathematics