In this task, you will be presented with a context from an academic paper and a question based on the context. You have to classify the questions into "Extractive", "Abstractive", or "Yes-no" questions. Extractive questions can be answered by concatenating extracts taken from a context into a summary while answering abstractive questions involves paraphrasing the context using novel sentences. Yes-no question is a question whose expected answer is one of two choices, one that affirms the question and one that denies the question. Typically, the choices are either yes or no.

[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.
Question: Is new evaluation metric extension of ROGUE?
[A]: Yes-no

[Q]: For all experiments, the dimensions of word embeddings and recurrent hidden states are both set to 512.
Question: Do they use the same architecture as LSTM-s and GRUs with just replacing with the LAU unit?
[A]: Yes-no

[Q]: To better analyze model generalization to an unseen, new domain as well as model leveraging the out-of-domain sources, we propose a new architecture which is an extension of the ARED model. In order to better select, aggregate and control the semantic information, a Refinement Adjustment LSTM-based component (RALSTM) is introduced to the decoder side. The proposed model can learn from unaligned data by jointly training the sentence planning and surface realization to produce natural language sentences.
Question: What is the difference of the proposed model with a standard RNN encoder-decoder?
[A]:
Abstractive