Advancements in abstractive text summarization: a deep learning approach

Abstract

With the rapid growth of data, text summarization has become vital for extracting key information efficiently. While extractive text summarization models are widely available, they often produce redundant outputs with limited capability of generating human-like summaries. Abstractive summarization, which generates new phrases and rephrases content, remains underexplored due to its complexity. This paper addresses this gap by developing an abstractive deep learning model using an encoder-decoder architecture supported with an attention mechanism. Trained on the dataset of Amazon Food Reviews, the model generates contextually rich and semantically accurate summaries. The model’s evaluation using BLEU and ROUGE metrics demonstrated promising results, with a score of 0.641 for BLEU, 0.520 for ROUGE-1, 0.345 for ROUGE-2, 0.461 for ROUGE-L and 0.428 for ROUGE-W, indicating coherence and structural integrity. This research highlights the potential of deep learning in addressing the limitations of classical methods and suggests opportunities for future advancements, such as scaling the model with larger datasets and integrating transformer-based techniques for improved summarization across diverse applications.