This paper presents abstractive text summarization using attentional encoder-decoder recurrent neural networks (RNNs) and explores further improvements. The authors propose several novel models to address key challenges in summarization, such as modeling keywords, capturing sentence-to-word structure, and generating rare or unseen words. They also introduce a new dataset for multi-sentence summaries and establish performance benchmarks. The work introduces an attentional encoder-decoder RNN, which outperforms state-of-the-art systems on two English corpora. The model uses a large vocabulary trick to reduce computational costs and improve convergence. A feature-rich encoder is proposed to incorporate linguistic features like POS tags, named entities, and TF-IDF statistics. A switching generator-pointer model is introduced to handle out-of-vocabulary words by either generating them or pointing to their location in the source document. A hierarchical attention model is also proposed to capture the hierarchical structure of documents. The models are evaluated on the Gigaword and DUC corpora, achieving state-of-the-art results. The switching generator-pointer model performs best on the test set, outperforming other models in multiple metrics. The hierarchical attention model shows marginal improvement over the baseline. The authors also introduce a new dataset for multi-sentence summaries, which presents interesting challenges for future research. The paper demonstrates that the proposed models significantly improve performance in abstractive summarization. The switching generator-pointer model is particularly effective in handling rare words, while the hierarchical attention model captures the hierarchical structure of documents. The results show that these models outperform previous approaches in terms of both accuracy and abstractive ability. The authors conclude that their models provide a strong foundation for further research in abstractive summarization.This paper presents abstractive text summarization using attentional encoder-decoder recurrent neural networks (RNNs) and explores further improvements. The authors propose several novel models to address key challenges in summarization, such as modeling keywords, capturing sentence-to-word structure, and generating rare or unseen words. They also introduce a new dataset for multi-sentence summaries and establish performance benchmarks. The work introduces an attentional encoder-decoder RNN, which outperforms state-of-the-art systems on two English corpora. The model uses a large vocabulary trick to reduce computational costs and improve convergence. A feature-rich encoder is proposed to incorporate linguistic features like POS tags, named entities, and TF-IDF statistics. A switching generator-pointer model is introduced to handle out-of-vocabulary words by either generating them or pointing to their location in the source document. A hierarchical attention model is also proposed to capture the hierarchical structure of documents. The models are evaluated on the Gigaword and DUC corpora, achieving state-of-the-art results. The switching generator-pointer model performs best on the test set, outperforming other models in multiple metrics. The hierarchical attention model shows marginal improvement over the baseline. The authors also introduce a new dataset for multi-sentence summaries, which presents interesting challenges for future research. The paper demonstrates that the proposed models significantly improve performance in abstractive summarization. The switching generator-pointer model is particularly effective in handling rare words, while the hierarchical attention model captures the hierarchical structure of documents. The results show that these models outperform previous approaches in terms of both accuracy and abstractive ability. The authors conclude that their models provide a strong foundation for further research in abstractive summarization.