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]: . While automated evaluation metrics like ROUGE measure lexical similarity between machine and human summaries, humans can better measure how coherent and readable a summary is. Our evaluation study investigates whether tuning the PG-net model increases summary coherence, by asking evaluators to select which of three summaries for the same document they like most: the PG-net model trained on CNN/DM; the model trained on student reflections; and finally the model trained on CNN/DM and tuned on student reflections. 20 evaluators were recruited from our institution and asked to each perform 20 annotations. Summaries are presented to evaluators in random order. Evaluators are then asked to select the summary they feel to be most readable and coherent. 
 Question: Who were the human evaluators used?
[EX A]: Extractive

[EX Q]: Querying posts on Twitter with extracted lexicons led to a collection of $19,300$ tweets. In order to have lexical diversity, we added 2500 randomly sampled tweets to our dataset. 
 Question: How many tweets were collected?
[EX A]: Extractive

[EX Q]: We evaluate our method on two widely-studied datasets provided by Waseem and Hovey BIBREF5 and Davidson et al. BIBREF9. 
 Question: Which publicly available datasets are used?
[EX A]:
Extractive