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: In this section, we evaluate the proposed method intrinsically in terms of whether the co-occurrence matrix after the low-rank approximation is able to capture similar concepts on student response data sets, and also extrinsically in terms of the end task of summarization on all corpora. In the following experiments, summary length is set to be the average number of words in human summaries or less. An alternative way to evaluate the hypothesis is to let humans judge whether two bigrams are similar or not, which we leave for future work. 
 Question: Do they quantitavely or qualitatively evalute the output of their low-rank approximation to verify the grouping of lexical items?
Example Output: Abstractive

Example Input: As a starting point, we used the DIP corpus BIBREF37 , a collection of 49 clusters of 100 web pages on educational topics (e.g. bullying, homeschooling, drugs) with a short description of each topic. 
 Question: Which collections of web documents are included in the corpus?
Example Output: Extractive

Example Input: We attribute the significant performance gain of our model over the baseline to a combination of several factors as described below: 
 Question: Do they train a different training method except from scheduled sampling?
Example Output:
Abstractive