This paper presents a comprehensive approach to abstractive text summarization using Attentional Encoder-Decoder Recurrent Neural Networks (RNNs). The authors demonstrate that their models achieve state-of-the-art performance on two different corpora. They propose several novel models to address critical problems in summarization, such as modeling key-words, capturing the hierarchy of sentence-to-word structure, and emitting rare or unseen words. The paper also introduces a new dataset for multi-sentence summarization and establishes performance benchmarks for future research. The main contributions include: 1. **Applying Attentional Encoder-Decoder RNNs**: The authors apply off-the-shelf attentional encoder-decoder RNNs, originally developed for machine translation, to abstractive summarization and show superior performance on two English corpora. 2. **Novel Models**: They propose several novel models that address specific challenges in summarization, such as capturing keywords, handling rare/unseen words, and modeling hierarchical document structure. 3. **New Dataset**: They introduce a new dataset for abstractive summarization of multi-sentence documents and establish performance benchmarks. The paper is organized into sections covering the introduction, models, related work, experiments and results, qualitative analysis, and conclusions. The experiments are conducted on the Gigaword, DUC, and CNN/Daily Mail corpora, with detailed results and comparisons to state-of-the-art models. The authors also provide insights into the limitations and future directions of their work.This paper presents a comprehensive approach to abstractive text summarization using Attentional Encoder-Decoder Recurrent Neural Networks (RNNs). The authors demonstrate that their models achieve state-of-the-art performance on two different corpora. They propose several novel models to address critical problems in summarization, such as modeling key-words, capturing the hierarchy of sentence-to-word structure, and emitting rare or unseen words. The paper also introduces a new dataset for multi-sentence summarization and establishes performance benchmarks for future research. The main contributions include: 1. **Applying Attentional Encoder-Decoder RNNs**: The authors apply off-the-shelf attentional encoder-decoder RNNs, originally developed for machine translation, to abstractive summarization and show superior performance on two English corpora. 2. **Novel Models**: They propose several novel models that address specific challenges in summarization, such as capturing keywords, handling rare/unseen words, and modeling hierarchical document structure. 3. **New Dataset**: They introduce a new dataset for abstractive summarization of multi-sentence documents and establish performance benchmarks. The paper is organized into sections covering the introduction, models, related work, experiments and results, qualitative analysis, and conclusions. The experiments are conducted on the Gigaword, DUC, and CNN/Daily Mail corpora, with detailed results and comparisons to state-of-the-art models. The authors also provide insights into the limitations and future directions of their work.