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.
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Question: We collect $37,263$ concept-sets as the inputs, each of which contains three to five common concepts. These concept-sets are sampled from several large corpora of image/video captions, such that the concepts inside them are more likely to co-occur in natural scenes. The expected concept-sets in our task are supposed to be likely co-occur in natural, daily-life scenes . The concepts in images/videos captions, which usually describe scenes in our daily life, thus possess the desired property. We therefore collect a large amount of caption sentences from a variety of datasets, including VATEX BIBREF4, LSMDC BIBREF12, ActivityNet BIBREF13, and SNLI BIBREF15, forming 1,040,330 sentences in total. 
 Question: Where do the concept sets come from?

Answer: These concept-sets are sampled from several large corpora of image/video captions


Question: The dataset consists of 198,112 news articles. 
 Question: How many articles did they have?

Answer: 198,112


Question: Follow the prior works BIBREF6, BIBREF7, BIBREF9, we adopt the BLEU and the Micro Entity F1 to evaluate our model performance.  We provide human evaluation on our framework and the compared models.  We hire several human experts and ask them to judge the quality of the responses according to correctness, fluency, and humanlikeness on a scale from 1 to 5. 
 Question: What were the evaluation metrics?

Answer:
BLEU Micro Entity F1 quality of the responses according to correctness, fluency, and humanlikeness on a scale from 1 to 5