In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

Ex Input:
The test-set accuracies obtained by different learning methods, including the current state-of-the-art results, are presented in Table TABREF11 . 
 Question: what models did they compare to?

Ex Output:
High-order CNN, Tree-LSTM, DRNN, DCNN, CNN-MC, NBoW and SVM 


Ex Input:
First, we adapt a dataset extracted from Freebase, i.e., FB24K, which used by BIBREF26. Then, we collect extra entities and relations that from DBpedia which that they should have at least 100 mentions BIBREF7 and they could link to the entities in the FB24K by the sameAs triples. Finally, we build a datasets named as DBP24K. In addition, we build a game datasets from our game knowledge graph, named as Game30K. 
 Question: What three datasets are used to measure performance?

Ex Output:
FB24K DBP24K Game30K


Ex Input:
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?

Ex Output:

 finding the important sentences from the story extracting the key information from those sentences using their AMR graphs