arXiv (Cornell University) · 2019 · 163 citations · 30 references
ComputingComputer ScienceHigh-performance ComputingComputational LinguisticsNatural Language ProcessingSequence AnalysisLanguage ModelingLanguage ModelsMachine TranslationNatural Language GenerationText GenerationText SummarizationAutomated Text GenerationMixed Precision TrainingNeural Text SummarizationSequence GenerationSequence Representation LearningSequence SummarizationSpeech-to-text TranslationSummarization Datasets
fairseq is an open-source sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling, and other text generation tasks. The toolkit is based on PyTorch and supports distributed training across multiple GPUs and machines. We also support fast mixed-precision training and inference on modern GPUs. A demo video can be found at https://www.youtube.com/watch?v=OtgDdWtHvto
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ROUGE: A Package for Automatic Evaluation of Summaries
Chin-Yew Lin · 2004 · 8.3K citations
Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond
Ramesh Nallapati, Bowen Zhou, Cícero dos Santos et al. · 2016 · 2.2K citations · Full text
Teaching Machines to Read and Comprehend
Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette et al. · arXiv (Cornell University) · 2015 · 1.5K citations · Full text