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: For those willing to have a more tightly-controlled installation of Seshat on their system, we also fully specify the manual installation steps in our online documentation). 
 Question: Is this software available to the public?
Example Output: Yes-no

Example Input: It thus seems feasible that by using a similar model, we can produce text samples that are conditioned upon a set of user-specified keywords. To illustrate by example, say a user desires the generated output to contain the keywords, $\lbrace subway, manhattan\rbrace $ .  
 Question: What are the user-defined keywords?
Example Output: Abstractive

Example Input: First, we introduce a two-stage labeling strategy for sentiment texts. In the first stage, annotators are invited to label a large number of short texts with relatively pure sentiment orientations. Each sample is labeled by only one annotator. In the second stage, a relatively small number of text samples with mixed sentiment orientations are annotated, and each sample is labeled by multiple annotators.  
 Question: What is the new labeling strategy?
Example Output:
Abstractive