TASK DEFINITION: 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.
PROBLEM: In our experiments, we used TF.IDF-based features over the title and over the content of the article we wanted to classify. We had these features twice – once for the title and once for the the content of the article, as we wanted to have two different representations of the same article. Thus, we used a total of 1,100 TF.IDF-weighted features (800 content + 300 title), limiting the vocabulary to the top 800 and 300 words, respectively (which occurred in more than five articles). 
 Question: what lexical features did they experiment with?

SOLUTION: TF.IDF-based features

PROBLEM: Using the distribution of the individual words in a category, we can compile distributions for the entire category, and therefore generate maps for these word categories.  
 Question: Do they build a model to automatically detect demographic, lingustic or psycological dimensons of people?

SOLUTION: No

PROBLEM: In particular, for the NLG task, our single model outperformed competing models in terms of both Rouge-L and Bleu-1. 
 Question: How do they measure the quality of summaries?

SOLUTION:
Rouge-L Bleu-1