A Unified Approach to Text Summarization: Classical, Machine Learning, and Deep Learning Methods
Aniket Kailas Shahade, Priyanka V. Deshmukh · Ingénierie des systèmes d information · 2025
The increase of text-based information on social media that occurs at the present time requires efficient summarization.Reducing text data is one of the most important tasks in Natural Language Processing, also known as Text Summarization.This paper gives a literature review of excluded and current summarization models with the excluded models including the extractive models which select some whole sentences and the abstractive models which paraphrase summaries.Also, it explains the basic statistical models such as TF-IDF or LSA, machine learning, and deep learning, and focuses on Transformer-based models like BERT or GPT, which have improved the summary quality.These findings also show a comparative analysis between deep learning models and other conventional techniques through other datasets.Open problems in summarization include cohesiveness, accuracy, and capturing long dependencies, the article introduces hybrids and pre-trained language models as possible solutions.The paper also indicates the possible research areas in the future including, the efficiency of the model, the enhancement of the factual contents of the model, and special purpose application of the model.This review has provided a good background for improving text summarization approaches and giving researchers and practitioners an idea of what is currently being done and what might be affected in the future.