MEEFO-Sum: Optimised Automatic Text Summarisation with Data Augmentation and Hybrid Feature Learning

One of the most important tasks in NLP (Natural Language Processing) is automatic text summarization which aims to distill vast amounts of textual information into a brief summary while maintaining its essential meaning. Notwithstanding notable progress earlier research in this field has been constrained by the incapacity to accurately summarize various datasets and capture contextual subtleties. Scalability generalization and striking a balance between summary coherence and informativeness are common issues with current models. Using the Conditional Generative Adversarial Network (CGAN) technique we present a novel framework for automatic text summarization that incorporates data augmentation in order to overcome these constraints. In order to ensure that the text is clean and prepared for additional analysis the suggested framework starts with a thorough pre-processing phase that includes tokenization hashtag and username elimination stop word removal punctuation removal stemming and lemmatization. The Robustly Optimised BERT Approach (RoBERTa) which we use for word embedding greatly improves contextual representation and text comprehension. A hybrid approach that combines ShuffleNet V2 and SE-ResNeXt-101 is used for feature extraction ensuring effective extraction of discriminative features. Ultimately the Modified Electric Eel Foraging Optimisation (MEEFO) algorithm is used to optimise the summary generation. This algorithm explores the trade-off between precision and recall in order to fine-tune the summary quality. Experimental results show that the suggested method is more effective than current approaches with notable gains in ROUGE-L scores and overall summary quality across a number of benchmark datasets. During training and testing the model achieves higher accuracy and lower loss demonstrating its generalizability and resilience.