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.

Input: Consider Input: The resulting dataset consists of 22,880 users, 41,094 blogs, and 561,003 posts. Table TABREF2 presents additional statistics of our dataset. 
 Question: How many users do they look at?

Output: Extractive


Input: Consider Input: We collect more human-written scenes for each concept-set in dev and test set through crowd-sourcing via the Amazon Mechanical Turk platform. Each input concept-set is annotated by at least three different humans. The annotators are also required to give sentences as the rationales, which further encourage them to use common sense in creating their scenes. 
 Question: Are the rationales generated after the sentences were written?

Output: Yes-no


Input: Consider Input: We use the following three evaluation metrics:

Phoneme Error Rate (PER) is the Levenshtein distance between the predicted phoneme sequences and the gold standard phoneme sequences, divided by the length of the gold standard phoneme sequences.

Word Error Rate (WER) is the percentage of words in which the predicted phoneme sequence does not exactly match the gold standard phoneme sequence.

Word Error Rate 100 (WER 100) is the percentage of words in the test set for which the correct guess is not in the first 100 guesses of the system. 
 Question: what evaluation metrics were used?


Output: Extractive