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]: Even if LangID-High does not present a more accurate result, it does present a more compact one: LangID-High is 15.4 MB, while the combined wFST high resource models are 197.5 MB. 
 Question: how is model compactness measured?
[EX A]: Abstractive

[EX Q]: Questions: We make use of the 7,787 science exam questions of the Aristo Reasoning Challenge (ARC) corpus BIBREF31 , which contains standardized 3rd to 9th grade science questions from 12 US states from the past decade.  
 Question: How was the dataset collected?
[EX A]: Abstractive

[EX Q]: The Zurich Cognitive Language Processing Corpus (ZuCo) 2.0, including raw and preprocessed eye-tracking and electroencephalography (EEG) data of 18 subjects, as well as the recording and preprocessing scripts, is publicly available. It contains physiological data of each subject reading 739 English sentences from Wikipedia (see example in Figure FIGREF1). During the recording session, the participants read 739 sentences that were selected from the Wikipedia corpus provided by culotta2006integrating.  
 Question: What kind of sentences were read?
[EX A]:
Extractive