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.

[EX Q]: The question generation model provides each candidate answer with a score by measuring semantic relevance between the question and the generated question based on the semantics of the candidate answer.
Question: Where is a question generation model used?
[EX A]: The question generation model provides each candidate answer with a score by measuring semantic relevance between the question and the generated question based on the semantics of the candidate answer.

[EX Q]: The results in Table TABREF7 show that SRCC performs much better in knowledge extraction. The two documents' contents contain the same idea expressed by terms in a different order that John had asked Mary to marry him before she left. It is obvious that cosine similarity cannot recognize this association, but SRCC has successfully recognized it and produced a similarity value of -0.285714.
Question: How do they evaluate knowledge extraction performance?
[EX A]: SRCC

[EX Q]: The participants are asked to score each summary on three indicators: relevance, conciseness and readability on a 1 to 5 scale, and rank the summary pair (tie allowed). We observe that SPNet reaches the highest score in both ROUGE and CIC
Question: What automatic and human evaluation metrics are used to compare SPNet to its counterparts?
[EX A]:
ROUGE and CIC relevance, conciseness and readability on a 1 to 5 scale, and rank the summary pair