Automated Summarization of Text Documents using Deep Belief Networks - A, Nithyakalyani; Jothilakshmi, S (Journal of Neonatal Surgery, 2025)
In recent times, text summarization process becomes familiar as it generates a summary comprising a significant sentence from the original document. At the same time, automated extractive text summarization for lecture notes have gained popularity, which commonly collects important key points and sentences. The absence of a text summarization technique for lecture notes in the Tamil language motivated us to perform this study. In this work, a new automated text summarization model for Tamil Lecture Notes using Deep Belief Networks (DBN) has been presented. The proposed DBN model performs several processes namely preprocessing, sentence feature extraction, optimal feature selection, and summarization. The application of NLP for sentence feature extraction and DBN for selecting the optimal feature vector from the feature sentence matrix results in effective lecture note summarization. The performance of the DBN model has been validated using lecture notes of different subjects in Tamil language. The obtained experimental outcome showcased the superior performance of the DBN model over the compared methods.