In this task, you will be presented with a context from an academic paper and a question based on the context. You have to classify the questions into "Extractive", "Abstractive", or "Yes-no" questions. Extractive questions can be answered by concatenating extracts taken from a context into a summary while answering abstractive questions involves paraphrasing the context using novel sentences. Yes-no question is a question whose expected answer is one of two choices, one that affirms the question and one that denies the question. Typically, the choices are either yes or no.

Example Input: Each game's video ranges from 30 to 50 minutes in length which contains image and chat data linked to the specific timestamp of the game. 
 Question: What is the average length of the recordings?
Example Output: Abstractive

Example Input: There are several caveats in our work: first, tweet sentiment is rarely binary (this work could be extended to a multinomial or continuous model). Second, our results are constrained to Twitter users, who are known to be more negative than the general U.S. population BIBREF9 . Third, we do not take into account the aggregate effects of continued natural disasters over time. Going forward, there is clear demand in discovering whether social networks can indicate environmental metrics in a “nowcasting" fashion. As climate change becomes more extreme, it remains to be seen what degree of predictive power exists in our current model regarding climate change sentiments with regards to natural disasters. 
 Question: Do the authors mention any confounds to their study?
Example Output: Yes-no

Example Input: These datasets are part of SemEval-2016 Challenge Task 5 BIBREF27 , BIBREF28 . Table TABREF7 shows the number of observations in each test corpus. 
 Question: what datasets were used in evaluation?
Example Output:
Extractive