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The importance of the generated questions is measured using Latent Dirichlet Allocation by identifying the subtopics (which are closely related to the original topic) in the given body of texts and applying the Extended String Subsequence Kernel to calculate their similarity with the questions. We also propose the use of syntactic tree kernels for the automatic judgment of the syntactic correctness of the questions. The questions are ranked by considering both their importance (in the context of the given body of texts) and syntactic correctness. To the best of our knowledge, no previous study has accomplished this task in our setting. A series of experiments demonstrate that the proposed topic-to-question generation approach can significantly outperform the state-of-the-art results.<\/jats:p>","DOI":"10.1162\/coli_a_00206","type":"journal-article","created":{"date-parts":[[2015,2,23]],"date-time":"2015-02-23T15:11:01Z","timestamp":1424704261000},"page":"1-20","source":"Crossref","is-referenced-by-count":54,"title":["Towards Topic-to-Question Generation"],"prefix":"10.1162","volume":"41","author":[{"given":"Yllias","family":"Chali","sequence":"first","affiliation":[{"name":"University of Lethbridge"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sadid A.","family":"Hasan","sequence":"additional","affiliation":[{"name":"Philips Research North America"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"281","reference":[{"key":"R1","unstructured":"Agarwal, M., R. Shah, and P. Mannem. 2011. Automatic question generation using discourse cues. 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