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Abstractive summarization, the task of generating a concise summary of input documents, requires: (1) reasoning over the source document to determine the salient pieces of information scattered across the long document, and (2) composing a cohesive text by reconstructing these salient facts into a shorter summary that faithfully reflects the complex relations connecting these facts.
Enhancing the transformer with explicit relational encoding for math problem solving
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Abstractive text summarization using sequence-to-sequence rnns and beyond
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Get to the point: Summarization with pointer-generator networks
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Attention is all you need
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Max Grusky, Mor Naaman, and Yoav Artzi. 2018 · 2018
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Qiuyuan Huang, Paul Smolensky, Xiaodong He, Li Deng, and Dapeng Wu. 2018 · 2018
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Kaiqiang Song, Lin Zhao, and Fei Liu. 2018 · 2018
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Scoring sentence singletons and pairs for abstractive summarization
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Abstract Meaning Representation for multi-document summarization
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Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization
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Question-answering with grammatically-interpretable representations
H. Palangi, P. Smolensky, X. He, and L. Deng. 2018 · 2018
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Multireward reinforced summarization with saliency and entailment
Ramakanth Pasunuru and Mohit Bansal. 2018 · 2018
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A deep reinforced model for abstractive summarization
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Yang Liu and Mirella Lapata. 2019 · 2019
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RNNs implicitly implement tensor-product representations
R. Thomas McCoy, Tal Linzen, Ewan Dunbar, and Paul Smolensky. 2019 · 2019
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Mapping natural-language problems to formal-language solutions using structured neural representations
Kezhen Chen, Qiuyuan Huang, Hamid Palangi, Paul Smolensky, Kenneth D Forbus, and Jianfeng Gao. 2020 · 2020
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FEQA: A question answering evaluation framework for faithfulness assessment in abstractive summarization
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020
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Pegasus: Pre-training with extracted gap-sentences for abstractive summarization
Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter J Liu. 2020 · 2020
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