SUMMAGAN: ENHANCING WEB NEWS SUMMARIZATION THROUGH GENERATIVE ADVERSARIAL NETWORKS
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".