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.

To simplify the task, we set up a binary classification: candidates who have been liked or shortlisted are considered part of the hirable class and others part of the not hirable class. 
 Question: How is "hirability" defined?
candidates who have been liked or shortlisted are considered part of the hirable class

. While automated evaluation metrics like ROUGE measure lexical similarity between machine and human summaries, humans can better measure how coherent and readable a summary is. Our evaluation study investigates whether tuning the PG-net model increases summary coherence, by asking evaluators to select which of three summaries for the same document they like most: the PG-net model trained on CNN/DM; the model trained on student reflections; and finally the model trained on CNN/DM and tuned on student reflections. 20 evaluators were recruited from our institution and asked to each perform 20 annotations. Summaries are presented to evaluators in random order. Evaluators are then asked to select the summary they feel to be most readable and coherent. 
 Question: Who were the human evaluators used?
20 evaluators were recruited from our institution and asked to each perform 20 annotations

For all the tasks in our experimental study, we use 36 millions English tweets collected between August and September 2017.  
 Question: Do they report results only on English datasets?

Yes