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:
For experiments, we train and evaluate our models in the civil law system of mainland China. We collect and construct a large-scale real-world data set of INLINEFORM0 case documents that the Supreme People's Court of People's Republic of China has made publicly available 
 Question: what is the size of the real-world civil case dataset?
answer:
Abstractive


question:
The dataset consists of queries and the corresponding image search results. Each token in each query is given a language tag based on the user-set home language of the user making the search on Google Images. 
 Question: Do the images have multilingual annotations or monolingual ones?
answer:
Abstractive


question:
In this step we extract the AMR graphs of the summary sentences using story sentence AMRs. We divide this task in two parts. First is finding the important sentences from the story and then extracting the key information from those sentences using their AMR graphs. 
 Question: How are sentences selected from the summary graph?
answer:

Extractive