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.

Example Input: 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?
Example Output: LEAD QUERY_SIM MultiMR SVR DocEmb ISOLATION

Example Input: The word intrusion test is expensive to apply since it requires manual evaluations by human observers separately for each embedding dimension. Furthermore, the word intrusion test does not quantify the interpretability levels of the embedding dimensions, instead it yields a binary decision as to whether a dimension is interpretable or not. However, using continuous values is more adequate than making binary evaluations since interpretability levels may vary gradually across dimensions. 
 Question: What advantages does their proposed method of quantifying interpretability have over the human-in-the-loop evaluation they compare to?
Example Output: it is less expensive and quantifies interpretability using continuous values rather than binary evaluations

Example Input: In order to validate the proposed classification model in real crowdsourcing settings, Amazon Mechanical Turk (AMT) was used to obtain labels from multiple annotators for two popular datasets: Reuters-21578 BIBREF30 and LabelMe BIBREF31 . In order to first validate the proposed model for classification problems in a slightly more controlled environment, the well-known 20-Newsgroups benchmark corpus BIBREF29 was used by simulating multiple annotators with different levels of expertise.  
 Question: what datasets were used?
Example Output:
Reuters-21578 BIBREF30  LabelMe BIBREF31 20-Newsgroups benchmark corpus BIBREF29