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]:  On high level, we indirectly evaluate the content relevance between the candidate summary and the human summary using information retrieval. To accomplish this, we use the summaries as search queries and compare the overlaps of the retrieved results.  
 Question: In the proposed metric, how is content relevance measured?
[EX A]: Abstractive

[EX Q]: In this section, we first describe a baseline method inspired by the “align to segment” of BIBREF12, BIBREF13. We then propose two extensions providing the model with a signal relevant to the segmentation process, so as to move towards a joint learning of segmentation and alignment. 
 Question: Does the paper report any alignment-only baseline?
[EX A]: Yes-no

[EX Q]: The minimal change operation substantially changed the meaning of the sentence, and yet the embedding of the transformation lies very closely to the original sentence (average similarity of 0.930). 
 Question: Do they do any analysis of of how the modifications changed the starting set of sentences?
[EX A]:
Yes-no