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: GPT-2 achieves the highest score and the $n$-gram the lowest. Transformer-XL and the LSTM LM perform in the middle, and at roughly the same level as each other. We report the 12-category accuracy results for all models and human evaluation in Table TABREF14. 
 Question: What is the performance of the models on the tasks?
Example Output: Abstractive

Example Input: We compare the performance of translation approaches based on four metrics:

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As in BIBREF20 , EM is 1 if a predicted plan matches exactly the ground truth; otherwise it is 0.

The harmonic average of the precision and recall over all the test set BIBREF26 .

The minimum number of insertions, deletions or swap operations required to transform a predicted sequence of behaviors into the ground truth sequence BIBREF27 .

GM is 1 if a predicted plan reaches the ground truth destination (even if the full sequence of behaviors does not match exactly the ground truth). Otherwise, GM is 0. 
 Question: What evaluation metrics are used?
Example Output: Abstractive

Example Input: In this study, we investigate the performance of our proposed models on WSJ BIBREF5 .  
 Question: Which dataset do they use?
Example Output:
Extractive

