Abstract: This paper describes RevUP which deals with automatically generating gap-fill questions. RevUP consists of 3 parts: Sentence Selection, Gap Selection & Multiple Choice Distractor Selection. To select topicallyimportant sentences from texts, we propose a novel sentence ranking method based on topic distributions obtained from topic models. To select gap-phrases from each selected sentence, we collected human annotations, using the Amazon Mechanical Turk, on the relative relevance of candidate gaps. This data is used to train a discriminative cla...
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Topics: 
Artificial intelligence
Natural language processing
Information retrieval