# Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond

arXiv: https://arxiv.org/abs/1602.06023 · HTML: https://mlsanity.com/paper/1602.06023/

## Key facts

- Cited by 812 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2016.

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## Abstract (arXiv)

In this work, we model abstractive text summarization using Attentional Encoder-Decoder Recurrent Neural Networks, and show that they achieve state-of-the-art performance on two different corpora. We propose several novel models that address critical problems in summarization that are not adequately modeled by the basic architecture, such as modeling key-words, capturing the hierarchy of sentence-to-word structure, and emitting words that are rare or unseen at training time. Our work shows that many of our proposed models contribute to further improvement in performance. We also propose a new dataset consisting of multi-sentence summaries, and establish performance benchmarks for further research.