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:
Since the input of our method is textual data, we follow the approach of BIBREF15 and map the text into a fixed-size vector representation. To this end, we use word embeddings that were successfully applied in other domains. We follow BIBREF5 and use pre-trained GloVe word vectors BIBREF16 to initialize the embedding layer (also known as look-up table). Section SECREF18 discusses the embedding layer in more details. 
 Question: Which pretrained word vectors did they use?
Answer:
 pre-trained GloVe word vectors 