750 citations · 1.6k across the 35 of their papers we have counts for
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Classify or Select: Neural Architectures for Extractive Document Summarization
Ramesh Nallapati, Bowen Zhou, Mingbo Ma
We present two novel and contrasting Recurrent Neural Network (RNN) based architectures for extractive summarization of documents. The Classifier based architecture sequentially ac…
SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents
Ramesh Nallapati, Feifei Zhai, Bowen Zhou
We present SummaRuNNer, a Recurrent Neural Network (RNN) based sequence model for extractive summarization of documents and show that it achieves performance better than or compara…
Pointing the Unknown Words
Caglar Gulcehre, Sungjin Ahn, Ramesh Nallapati +2
The problem of rare and unknown words is an important issue that can potentially influence the performance of many NLP systems, including both the traditional count-based and the d…
Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond
Ramesh Nallapati, Bowen Zhou, Cicero Nogueira dos santos +2
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 di…