In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

[EX Q]: However, the coherence of the topics produced by LDA is poorer than expected.

To address this lack of coherence, we applied non-negative matrix factorization (NMF).
[EX A]: How are prominent topics idenified in Dabiq and Rumiyah?

[EX Q]: CFQ contains 239,357 English question-answer pairs that are answerable using the public Freebase data.
[EX A]: How big is new question answering dataset?

[EX Q]: We also explored clustering 12742 STRENGTH sentences directly using CLUTO BIBREF19 and Carrot2 Lingo BIBREF20 clustering algorithms. 
[EX A]:
What clustering algorithms were used?