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.

Example Input: Observe that in only about 12% (7 of the top 59) of the most cited areas of research, women received higher average citations than men. These include: sentiment analysis, information extraction, document summarization, spoken dialogue, cross lingual (research), dialogue, systems, language generation. (Of course, note that some of the 59 areas, as estimated using title term bigrams, are overlapping. Also, we did not include large scale in the list above because the difference in averages is very small and it is not really an area of research.) Thus, the citation gap is common across a majority of the high-citations areas within NLP.
Example Output: Which NLP area have the highest average citation for woman author?

Example Input: Previous attempts to annotate QA-SRL initially involved trained annotators BIBREF4 but later resorted to crowdsourcing BIBREF5 to achieve scalability.
Example Output: How was the corpus obtained?

Example Input: Specifically, we design three tasks for seq2seq model pre-training, namely Sentence Reordering (SR), Next Sentence Generation (NSG), and Masked Document Generation (MDG). 
Example Output:
What is masked document generation?