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.

Above all, we introduce two common baselines. The first one just selects the leading sentences to form a summary. It is often used as an official baseline of DUC, and we name it “LEAD”. The other system is called “QUERY_SIM”, which directly ranks sentences according to its TF-IDF cosine similarity to the query. Above all, we introduce two common baselines. The first one just selects the leading sentences to form a summary. It is often used as an official baseline of DUC, and we name it “LEAD”. The other system is called “QUERY_SIM”, which directly ranks sentences according to its TF-IDF cosine similarity to the query. In addition, we implement two popular extractive query-focused summarization methods, called MultiMR BIBREF2 and SVR BIBREF20 . Since our model is totally data-driven, we introduce a recent summarization system DocEmb BIBREF9 that also just use deep neural network features to rank sentences. To verify the effectiveness of the joint model, we design a baseline called ISOLATION, which performs saliency ranking and relevance ranking in isolation. 
 Question: What models do they compare to?
LEAD QUERY_SIM MultiMR SVR DocEmb ISOLATION

The CoNLL-2012 shared task BIBREF21 corpus is used as the evaluation dataset, which is selected from the Ontonotes 5.0. 
 Question: What dataset do they evaluate their model on?
CoNLL-2012 shared task BIBREF21 corpus

Our search space consists of two stackable cells, one for the model encoder and one for the decoder Each cell contains NASNet-style blocks, which receive two hidden state inputs and produce new hidden states as outputs  Our search space contains five branch-level search fields (input, normalization, layer, output dimension and activation), one block-level search field (combiner function) and one cell-level search field (number of cells). 
 Question: What is in the model search space?

Our search space consists of two stackable cells, one for the model encoder and one for the decoder  Each cell contains NASNet-style blocks, which receive two hidden state inputs and produce new hidden states as outputs Our search space contains five branch-level search fields (input, normalization, layer, output dimension and activation), one block-level search field (combiner function) and one cell-level search field (number of cells).