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.

[EX Q]: We consider 5 major profile attributes for further analysis. These attributes are username, display name, profile image, location and description respectively. 
 Question: What profile metadata is used for this analysis?
[EX A]: Extractive

[EX Q]: In order to understand the latent topics of those #MeToo tweets for college followers, we first utilize Latent Dirichlet Allocation (LDA) to label universal topics demonstrated by the users. Since certain words frequently appear in those #MeToo tweets (e.g., sexual harassment, men, women, story, etc.), we transform our corpus using TF-IDF, a term-weighting scheme that discounts the influence of common terms. 
 Question: How are the topics embedded in the #MeToo tweets extracted?
[EX A]: Abstractive

[EX Q]: We evaluate a multi-task approach and three algorithms that explicitly model the task dependencies. 
 Question: Will these findings be robust through different datasets and different question answering algorithms?
[EX A]:
Yes-no