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: We used a dataset provided by Shortir, an Indonesian news aggregator and summarizer company. The dataset contains roughly 20K news articles. Each article has the title, category, source (e.g., CNN Indonesia, Kumparan), URL to the original article, and an abstractive summary which was created manually by a total of 2 native speakers of Indonesian 
 Question: Did they use a crowdsourcing platform for the summaries?
Example Output: Yes-no

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

[align=left,leftmargin=0em,labelsep=0.4em,font=]

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 section we present two datasets used in our experiments: The NowThisNews dataset, collected for the purpose of this paper, and The BreakingNews dataset BIBREF4 , publicly available dataset of news articles.

contains 4090 posts with associated videos from NowThisNews Facebook page collected between 07/2015 and 07/2016. For each post we collected its title and the number of views of the corresponding video, which we consider our popularity metric. Due to a fairly lengthy data collection process, we decided to normalize our data by first grouping posts according to their publication month and then labeling the posts for which the popularity metric exceeds the median monthly value as popular, the remaining part as unpopular. 
 Question: Where do they obtain the news videos from?
Example Output:
Extractive