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]:  Last, we evaluate our approaches in 9 commonly used text classification datasets. We evaluate our methods on several commonly used datasets whose themes range from sentiment, web-page, science to medical and healthcare. 
 Question: What NLP tasks do they consider?
[EX A]: Abstractive

[EX Q]: In a recent work on sentiment analysis in Turkish BIBREF10, they learn embeddings using Turkish social media. They use the word2vec algorithm, create several unsupervised hand-crafted features, generate document vectors and feed them as input into the support vector machines (SVM) approach. We outperform this baseline approach using more effective word embeddings and supervised hand-crafted features. 
 Question: What baseline method is used?
[EX A]: Abstractive

[EX Q]: Thus, we can distinguish two main regions or clusters in the maps. The purple background covers most of the map and represents rural regions with small, scattered population. Our analysis shows that this group of cells possesses more specific words in their lexicon. In contrast, the green and yellow cells form a second cluster that is largely concentrated on the center and along the coastline, which correspond to big cities and industrialized areas.  In these cells, the use of standard Spanish language is widespread due probably to school education, media, travelers, etc. 
 Question: Do the authors mention any possible confounds in their study?
[EX A]:
Yes-no