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.
One 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?
Solution is here: No
Explanation: Based on the context, the dataset is constructed from a famous Chinese social media microblogging service Weibo.

Now, solve this: BIBREF25 also proposed the idea of using average of word embeddings to represent the global context of a document. Different from their work, we choose to corrupt the original document by randomly removing significant portion of words, and represent the document using only the embeddings of the words remained. 
 Question: Is their approach similar to making an averaged weighted sum of word vectors, where weights reflect word frequencies?
Solution:
Different from their work, we choose to corrupt the original document by randomly removing significant portion of words, and represent the document using only the embeddings of the words remained.