arXiv (Cornell University) · 2019 · 83 citations · 57 references
ComputingComputer ScienceArtificial IntelligenceComputational LinguisticsNatural Language ProcessingArtificial Intelligence In Language LearningLanguage ModelingNatural Language GenerationNatural Language UnderstandingQuestion AnsweringText GenerationAnswer GenerationHuman IntelligenceNeural Question AnsweringQuestion Answering CorporaQuestion Answering SystemsQuestion Generation
Emerging research in Neural Question Generation (NQG) has started to integrate a larger variety of inputs, and generating questions requiring higher levels of cognition. These trends point to NQG as a bellwether for NLP, about how human intelligence embodies the skills of curiosity and integration. We present a comprehensive survey of neural question generation, examining the corpora, methodologies, and evaluation methods. From this, we elaborate on what we see as emerging on NQG's trend: in terms of the learning paradigms, input modalities, and cognitive levels considered by NQG. We end by pointing out the potential directions ahead.
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Kishore Papineni, Salim Roukos, Todd J. Ward et al. · 2001 · 21.5K citations · Full text
ROUGE: A Package for Automatic Evaluation of Summaries
Chin-Yew Lin · 2004 · 8.3K citations