This paper introduces a novel architecture for abstractive text summarization, combining a pointer-generator network with a coverage mechanism to improve accuracy and reduce repetition. The pointer-generator network allows the model to both copy words from the source text and generate new words, while the coverage mechanism ensures that the model does not repeat information. The model is applied to the CNN/Daily Mail dataset, achieving a significant improvement over the current state-of-the-art abstractive summarization system, outperforming it by at least 2 ROUGE points. The paper discusses the challenges of abstractive summarization, including the difficulty of accurately reproducing factual details and avoiding repetition. The proposed architecture addresses these issues by using a hybrid pointer-generator network that can copy words from the source text via pointing, and a coverage mechanism that tracks what has been summarized to discourage repetition. The model is trained on the CNN/Daily Mail dataset, which contains news articles paired with multi-sentence summaries. The paper also compares the proposed model with existing abstractive and extractive summarization methods, showing that the pointer-generator model achieves better ROUGE and METEOR scores than the baseline model. The coverage mechanism further improves the model's performance by reducing repetition. The paper also discusses the limitations of the model, including its inability to surpass the ROUGE scores of the lead-3 baseline and the current best extractive model. The paper concludes that the proposed architecture is a significant improvement over existing abstractive summarization methods, but further research is needed to achieve higher levels of abstraction. The model's ability to copy words from the source text and generate new words makes it more flexible than extractive methods, but it still has limitations in terms of abstraction. The paper also highlights the importance of the coverage mechanism in reducing repetition and improving the quality of the summaries.This paper introduces a novel architecture for abstractive text summarization, combining a pointer-generator network with a coverage mechanism to improve accuracy and reduce repetition. The pointer-generator network allows the model to both copy words from the source text and generate new words, while the coverage mechanism ensures that the model does not repeat information. The model is applied to the CNN/Daily Mail dataset, achieving a significant improvement over the current state-of-the-art abstractive summarization system, outperforming it by at least 2 ROUGE points. The paper discusses the challenges of abstractive summarization, including the difficulty of accurately reproducing factual details and avoiding repetition. The proposed architecture addresses these issues by using a hybrid pointer-generator network that can copy words from the source text via pointing, and a coverage mechanism that tracks what has been summarized to discourage repetition. The model is trained on the CNN/Daily Mail dataset, which contains news articles paired with multi-sentence summaries. The paper also compares the proposed model with existing abstractive and extractive summarization methods, showing that the pointer-generator model achieves better ROUGE and METEOR scores than the baseline model. The coverage mechanism further improves the model's performance by reducing repetition. The paper also discusses the limitations of the model, including its inability to surpass the ROUGE scores of the lead-3 baseline and the current best extractive model. The paper concludes that the proposed architecture is a significant improvement over existing abstractive summarization methods, but further research is needed to achieve higher levels of abstraction. The model's ability to copy words from the source text and generate new words makes it more flexible than extractive methods, but it still has limitations in terms of abstraction. The paper also highlights the importance of the coverage mechanism in reducing repetition and improving the quality of the summaries.