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.
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Question: We leveraged an existing, annotated Twitter dataset that was constructed based on a hierarchical model of depression-related symptoms BIBREF12 , BIBREF13 . The dataset contains 9,473 annotations for 9,300 tweets. 
 Question: What dataset is used for this study?

Answer: Extractive


Question: With BERT, two fully unsupervised tasks are performed. The Masked Language Model and the Next Sentence Prediction (NSP).

For this study, the NSP is used as a proxy for the relevance of response. 
 Question: was bert used?

Answer: Yes-no


Question: For the evaluation of summaries we use the standard ROGUE metric. For comparison with previous AMR based summarization methods, we report the Recall, Precision and INLINEFORM0 scores for ROGUE-1. 
 Question: Which evaluation methods are used?

Answer:
Abstractive