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

Understand

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.