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A Novel Framework for Automatic Chinese Question Generation Based on Multi-Feature Neural Network Model
Computer Science and Information Systems, Tome 15 (2018) no. 3.

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Automatic question generation from text or paragraph is a great challenging task which attracts broad attention in natural language processing. Because of the verbose texts and fragile ranking methods, the quality of top generated questions is poor. In this paper, we present a novel framework Automatic Chinese Question Generation (ACQG) to generate questions from text or paragraph. In ACQG, we use an adopted TextRank to extract key sentences and a template-based method to construct questions from key sentences. Then a multi-feature neural network model is built for ranking to obtain the top questions. The automatic evaluation result reveals that the proposed framework outperforms the state-of-the-art systems in terms of perplexity. In human evaluation, questions generated by ACQG rate a higher score.
Keywords: Chinese Question Generation, TextRank, Multi-Feature Neural Network Model 
@article{CSIS_2018_15_3_a3,
     author = {Hai-Tao Zheng and Jinxin Han and Jinyuan Chen and Arun Kumar Sangaiah},
     title = {A {Novel} {Framework} for {Automatic} {Chinese} {Question} {Generation} {Based} on {Multi-Feature} {Neural} {Network} {Model}},
     journal = {Computer Science and Information Systems},
     publisher = {mathdoc},
     volume = {15},
     number = {3},
     year = {2018},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2018_15_3_a3/}
}
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Hai-Tao Zheng; Jinxin Han; Jinyuan Chen; Arun Kumar Sangaiah. A Novel Framework for Automatic Chinese Question Generation Based on Multi-Feature Neural Network Model. Computer Science and Information Systems, Tome 15 (2018) no. 3. http://geodesic.mathdoc.fr/item/CSIS_2018_15_3_a3/