instruction:
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.
question:
CIC is a suitable complementary metric to ROUGE because it accounts for the most important information within each dialog domain. CIC can be applied to any summarization task with predefined essential entities. 
 Question: Is new evaluation metric extension of ROGUE?
answer:
Yes-no


question:
We use three datasets for our experiments: IWSLT14 German-English, Turkish-English, and WMT14 English-German. 
 Question: what datasets were used?
answer:
Abstractive


question:
We evaluate the QRNN architecture on a popular document-level sentiment classification benchmark, the IMDb movie review dataset BIBREF17 . The dataset consists of a balanced sample of 25,000 positive and 25,000 negative reviews, divided into equal-size train and test sets, with an average document length of 231 words BIBREF18 . We compare only to other results that do not make use of additional unlabeled data (thus excluding e.g., BIBREF19 ). 
 Question: What sentiment classification dataset is used?
answer:

Extractive