This paper introduces COPYNET, a sequence-to-sequence (Seq2Seq) model that incorporates a copying mechanism to selectively replicate segments from the input sequence in the output. The copying mechanism allows the model to directly copy sub-sequences from the input, which is particularly useful when the output requires exact replication of specific parts of the input, such as entity names or dates. COPYNET is designed to integrate both the regular word generation process and the copying mechanism, enabling it to handle both understanding and rote memorization in a unified framework. The model is trained end-to-end using gradient descent and is shown to outperform traditional RNN-based models on tasks such as text summarization and dialogue systems. COPYNET is based on an encoder-decoder architecture with an attention mechanism. The encoder transforms the input sequence into a series of hidden states, which are then used by the decoder to generate the output sequence. The decoder uses a mixture of generate-mode and copy-mode to predict the next word in the output sequence. In generate-mode, the model predicts words from a vocabulary, while in copy-mode, it copies words directly from the input sequence. The model uses a selective read mechanism to locate and copy specific parts of the input sequence, ensuring high fidelity in the output. The paper presents empirical studies on synthetic data, text summarization, and single-turn dialogue tasks, demonstrating that COPYNET significantly outperforms traditional models. On synthetic data, COPYNET effectively learns to replicate long subsequences of symbols. In text summarization, COPYNET achieves higher ROUGE scores compared to other models, indicating better performance in capturing the core meaning of the original text. In dialogue tasks, COPYNET accurately replicates critical segments from the input and generates the rest of the response using the generate-mode, showing its ability to handle both copying and generation. The paper also discusses related work, including Pointer Networks and efforts to address the out-of-vocabulary (OOV) problem in neural machine translation. COPYNET is shown to be more flexible and general compared to these approaches, as it can handle a wide range of tasks and is not limited to specific types of OOV words. The model's ability to copy exact source words makes it suitable for applications where precise replication is required, such as machine translation. Overall, COPYNET provides a novel and effective approach to incorporating copying into Seq2Seq learning, enhancing the model's ability to handle complex tasks that require both understanding and rote memorization.This paper introduces COPYNET, a sequence-to-sequence (Seq2Seq) model that incorporates a copying mechanism to selectively replicate segments from the input sequence in the output. The copying mechanism allows the model to directly copy sub-sequences from the input, which is particularly useful when the output requires exact replication of specific parts of the input, such as entity names or dates. COPYNET is designed to integrate both the regular word generation process and the copying mechanism, enabling it to handle both understanding and rote memorization in a unified framework. The model is trained end-to-end using gradient descent and is shown to outperform traditional RNN-based models on tasks such as text summarization and dialogue systems. COPYNET is based on an encoder-decoder architecture with an attention mechanism. The encoder transforms the input sequence into a series of hidden states, which are then used by the decoder to generate the output sequence. The decoder uses a mixture of generate-mode and copy-mode to predict the next word in the output sequence. In generate-mode, the model predicts words from a vocabulary, while in copy-mode, it copies words directly from the input sequence. The model uses a selective read mechanism to locate and copy specific parts of the input sequence, ensuring high fidelity in the output. The paper presents empirical studies on synthetic data, text summarization, and single-turn dialogue tasks, demonstrating that COPYNET significantly outperforms traditional models. On synthetic data, COPYNET effectively learns to replicate long subsequences of symbols. In text summarization, COPYNET achieves higher ROUGE scores compared to other models, indicating better performance in capturing the core meaning of the original text. In dialogue tasks, COPYNET accurately replicates critical segments from the input and generates the rest of the response using the generate-mode, showing its ability to handle both copying and generation. The paper also discusses related work, including Pointer Networks and efforts to address the out-of-vocabulary (OOV) problem in neural machine translation. COPYNET is shown to be more flexible and general compared to these approaches, as it can handle a wide range of tasks and is not limited to specific types of OOV words. The model's ability to copy exact source words makes it suitable for applications where precise replication is required, such as machine translation. Overall, COPYNET provides a novel and effective approach to incorporating copying into Seq2Seq learning, enhancing the model's ability to handle complex tasks that require both understanding and rote memorization.