In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

[Q]: CIC is a suitable complementary metric to ROUGE because it accounts for the most important information within each dialog domain. CIC can be applied to any summarization task with predefined essential entities.
[A]: Is new evaluation metric extension of ROGUE?

[Q]: Our work is further connected to the topic of interpretability and transparency of machine learning models BIBREF11, BIBREF35, BIBREF12, for which humans are increasingly involved, for instance for post-hoc evaluations of the model's interpretability.
[A]: How are the interpretability merits of the approach demonstrated?

[Q]: The embedding-wise convolution is to apply a convolution filter w $\in \mathbb {R}^{\mathcal {\}k*d}$ to a window of $k$ word embeddings to generate a new feature, i.e., summarizing a local context of $k$ words. By applying the convolutional filter to all possible windows in the sentence, a feature map $c$ will be generated.
[A]:
What supports the claim that injected CNN into recurent units will enhance ability of the model to catch local context and reduce ambiguities?