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:
The first model, referred as 'LSTM (PTB)' in the following sections, was trained on the sentences from Penn Treebank BIBREF12. The second model, referred as 'LSTM (FTB)', was trained on the sentences from French Treebank BIBREF13. We incorporate two pretrained English language models: one trained on the Billion Word benchmark (referred as 'LSTM (1B)') from BIBREF14, and the other trained on English Wikipedia (referred as 'LSTM (enWiki)') from BIBREF3. For French, we trained a large LSTM language model (referred as 'LSTM (frWaC)') on a random subset (about 4 million sentences, 138 million word tokens) of the frWaC dataset BIBREF15.
Question: What is the size of the datasets employed?
Answer:
(about 4 million sentences, 138 million word tokens) one trained on the Billion Word benchmark