This paper addresses the shortcomings of neural sequence-to-sequence models in abstractive text summarization, which often reproduce factual details inaccurately and tend to repeat themselves. The authors propose a novel architecture that enhances the standard sequence-to-sequence attentional model in two ways: first, by using a hybrid pointer-generator network that can copy words from the source text, improving accuracy and handling out-of-vocabulary (OOV) words while retaining the ability to generate new words; second, by incorporating coverage to track what has been summarized, which helps prevent repetition. The model is applied to the CNN/Daily Mail summarization task, outperforming the current state-of-the-art abstractive system by at least 2 ROUGE points. The paper also discusses related work, experimental setup, and observations, highlighting the effectiveness of the proposed approach in reducing inaccuracies and repetition.This paper addresses the shortcomings of neural sequence-to-sequence models in abstractive text summarization, which often reproduce factual details inaccurately and tend to repeat themselves. The authors propose a novel architecture that enhances the standard sequence-to-sequence attentional model in two ways: first, by using a hybrid pointer-generator network that can copy words from the source text, improving accuracy and handling out-of-vocabulary (OOV) words while retaining the ability to generate new words; second, by incorporating coverage to track what has been summarized, which helps prevent repetition. The model is applied to the CNN/Daily Mail summarization task, outperforming the current state-of-the-art abstractive system by at least 2 ROUGE points. The paper also discusses related work, experimental setup, and observations, highlighting the effectiveness of the proposed approach in reducing inaccuracies and repetition.