Sequence-to-Sequence RNNs for Text Summarization

By Ramesh Nallapati, Bing Xiang, Bowen Zhou. 2016

166 citations recorded by the source corpus.

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Citations in the corpus

Unsupervised Pretraining for Sequence to Sequence Learning (2016, methodology): “Following Nallapati et al. (2016), we modify the data collection scripts to restore the bullet point summaries.”
Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond (2016, neutral): “Evaluation metrics: Similar to (Nallapati et al., 2016) and (Chopra et al.”
Abstractive Sentence Summarization with Attentive Recurrent Neural Networks (2016, methodology): “, 2003; Napoles et al., 2012) and we use only the annotations for tokenization and sentence separation while discarding other annotations such as tags and parses. We pair the first sentence of each article with its headline to form sentence-summary pairs. The data is pre-processed in the same way as”
SummaRuNNer: A Recurrent Neural Network Based Sequence Model for Extractive Summarization of Documents (2016, methodology): “In addition, we also compare with the state-of-the art deep learning models from (Cheng and Lapata 2016) and (Nallapati et al. 2016).”
Controlling Output Length in Neural Encoder-Decoders (2016, background): “Nallapati et al. (2016) investigate a way to solve many important problems capturing keywords, or inputting multiple sentences.”
Zero-Shot Cross-lingual Classification Using Multilingual Neural Machine Translation (2018, neutral): “The tasks include sentiment analysis (Socher et al. 2013), entailment (Bowman et al. 2015), summarization (Nallapati, Xiang, and Zhou 2016), question answering (Liu et al. 2018), and semantic role labeling (He et al. 2017).”
Cutting-off Redundant Repeating Generations for Neural Abstractive Summarization (2016, methodology): “Finally, we evaluate the effectiveness of our method on well-studied ABS benchmark data provided by Rush et al. Rush et al. (2015), and evaluated in Chopra et al. (2016); Nallapati et al. (2016b); Kikuchi et al. (2016); Takase et al. (2016); Ayana et al. (2016); Gulcehre et al. (2016).”
Neural Headline Generation with Sentence-wise Optimization (2016, methodology): “Following (Rush et al., 2015; Chopra et al., 2016; Nallapati et al., 2016), for DUC2003 and DUC2004, we report recall scores of ROUGE1(R1R) , ROUGE-2(R2R) and ROUGE-L(RLR) with official 75 bytes ceiling limit.”
Abstract Text Summarization with a Convolutional Seq2seq Model (2019, methodology): “[25] utilized a similar seq2seq model with RNN encoder and decoder, but it contained English corpora, reporting advanced results on DUC and Gigaword datasets.”
Abstractive Dialogue Summarization with Sentence-Gated Modeling Optimized by Dialogue Acts (2018, methodology): “Extractive methods assemble the summary from the source text directly [1, 2, 3], while abstractive methods generate words to form the summary [4, 5, 6].”
Efficient Attention using a Fixed-Size Memory Representation (2017, background): “, 2016), text summarization (Rush et al., 2015; Nallapati et al., 2016), speech recognition (Chan et al.”