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.

Let me give you an example: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chinese Short Text Summarization Dataset (LCSTS) is constructed by BIBREF1 . The dataset consists of more than 2.4 million text-summary pairs in total, constructed from a famous Chinese social media microblogging service Weibo.  
 Question: Are results reported only for English data?
The answer to this example can be: No
Here is why: Based on the context, the dataset is constructed from a famous Chinese social media microblogging service Weibo.

OK. solve this:
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:

Show your work and conclude with the exact phrasing "Therefore, the final answer is [answer]." where [answer] is just the final number or expression that solves the problem.
user
 finding the important sentences from the story extracting the key information from those sentences using their AMR graphs