Deep Learning Methods for Text Summarization

Abstract

This review evaluates recent advancements in deep learning methods for text summarization, a key Natural Language Processing (NLP) task driven by the explosion of textual data. The goal is to generate concise summaries while preserving the core meaning of original texts. We analyze key deep learning architectures including Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Transformers using a systematic literature review (SLR) approach. Additionally, attention mechanisms, pointer-generator networks, and beam search techniques are explored, along with benchmark datasets such as CNN/Daily Mail and Gigaword. Transformer-based models consistently achieve superior performance in abstractive summarization, as evidenced by higher ROUGE-1 (43.9), ROUGE-2 (20.3), and ROUGE-L (40.1) scores on benchmark datasets like CNN/Daily Mail, compared to RNN and LSTM models. This performance gain is attributed to the self-attention mechanism and parallel sequence processing capabilities of Transformer architectures. Deep learning has transformed summarization, with Transformer models demonstrating clear advantages over earlier approaches. Future work should focus on hybrid models that integrate extractive and abstractive techniques, alongside developing more robust evaluation metrics that align closely with human judgment beyond n-gram overlap.