SUMMAGAN: ENHANCING WEB NEWS SUMMARIZATION THROUGH GENERATIVE ADVERSARIAL NETWORKS

This paper introduces SummaGAN, a novel application of Generative Adversarial Networks (GANs) for text summarization. Unlike traditional summarization methods that rely on extractive techniques, SummaGAN uses adversarial learning to generate coherent and contextually accurate summaries. The model includes a transformer-based generator that creates summaries and a discriminator that evaluates their quality, guiding the generator to produce outputs that closely mimic human-written summaries. A large, diverse dataset of over 100,000 articles from domains such as news, scientific literature, and blogs was used to train and fine-tune the model. Experimental results show that SummaGAN significantly outperforms existing baseline models, including traditional extractive summarizers and advanced abstractive models, across multiple evaluation metrics such as ROUGE, BLEU, METEOR, and the newly introduced Coherence and Consistency Score (CCS). SummaGAN achieved a 15% improvement in ROUGE-1 scores and a 20% enhancement in BLEU scores, indicating better summary relevance and fluency. The CCS metric highlights SummaGAN's superior ability to maintain the logical flow and factual accuracy of the source text. This research demonstrates the potential of GANs to address challenges in text summarization, such as redundancy and loss of meaning, through dynamic adversarial learning. The integration of GANs with transformer architectures presents a robust framework for future NLP advancements. Future research will explore scaling the model for larger datasets, applying it in multilingual contexts, and refining the adversarial training process for improved efficiency and performance.

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