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 Input:
Results form Table TABREF31 may give an idea that INLINEFORM0 is just an scaled INLINEFORM1 . While it is true that they show a linear correlation, INLINEFORM2 may produce a different system ranking than INLINEFORM3 given the integral multi-reference principle it follows. However, what we consider the most profitable about INLINEFORM4 is the twofold inclusion of all available references it performs. First, the construction of INLINEFORM5 to provide a more inclusive reference against to whom be evaluated and then, the computation of INLINEFORM6 , which scales the result depending of the agreement between references. We showed how INLINEFORM0 is an inclusive metric which not only evaluates the performance of a system against all references, but also takes into account the agreement between them.  
 Question: What makes it a more reliable metric?

Ex Output:
Abstractive


Ex Input:
Since none of the datasets from previous works have been published, we decide to build a new one. We randomly collect INLINEFORM0 cases from China Judgments Online, among which INLINEFORM1 cases are for training, INLINEFORM2 each for validation and testing. 
 Question: what datasets are used in the experiment?

Ex Output:
Extractive


Ex Input:
In order to make the corpus collection easier and faster, we adopted a semi-automatic procedure based on sequential neural models BIBREF19, BIBREF20. Instead, since tokens are transcribed at morpheme level, we split Arabish tokens into characters, and Arabic tokens into morphemes, and we treated each token itself as a sequence. Our model learns thus to map Arabish characters into Arabic morphemes. With this model we automatically transcribed into Arabic morphemes, roughly, 5,000 additional tokens, corresponding to the second annotation block.  Manual transcription plus a 
 Question: How does the semi-automatic construction process work?

Ex Output:

Abstractive