Abstractive Text Summarization using Sequence to sequence RNNs and Beyond
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.
CoNLL