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]: For each task, the training data that was made available by the organizers is used, which is a selection of tweets with for each tweet a label describing the intensity of the emotion or sentiment BIBREF1 . Since the present study focuses on Spanish tweets, all tweets from the English datasets were translated into Spanish. This new set of "Spanish" data was then added to our original training set. Again, the machine translation platform Apertium BIBREF5 was used for the translation of the datasets.
Question: What dataset did they use?
[EX A]: Abstractive

[EX Q]: Table TABREF15 shows a comparison of the results on SimCluster versus K-means algorithm. Here our SimCluster algorithm improves the F1-scores from 0.412 and 0.417 in the two domains to 0.442 and 0.441. The ARI scores also improve from 0.176 and 0.180 to 0.203 and 0.204.
Question: Do they use the same distance metric for both the SimCluster and K-means algorithm?
[EX A]: Yes-no

[EX Q]: we take the CORD-19 dataset BIBREF2, which contains over 45,000 scholarly articles, including over 33,000 with full text, about COVID-19, SARS-CoV-2, and related coronaviruses. We develop sentence classification methods to identify all sentences narrating radiological findings from COVID-19. We conduct experiments to verify the effectiveness of our method. From the CORD-19 dataset, our method successfully discovers a set of clinical findings that are closely related with COVID-19.
Question: How large is the collection of COVID-19 literature?
[EX A]:
Extractive