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.

Ex Input:
. 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?

Ex Output:
20 evaluators were recruited from our institution and asked to each perform 20 annotations


Ex Input:
To fine-tune BERT, we used a proprietary corpus that consists of hundreds of thousands of legal agreements. 
 Question: How big is dataset used for fine-tuning BERT?

Ex Output:
hundreds of thousands of legal agreements


Ex Input:
For evaluation, the Visual Dialog task employs four metrics. NDCG is the primary metric of the Visual Dialog Challenge which considers multiple similar answers as correct ones. The other three are MRR, recall@k, and mean rank where they only consider the rank of a single answer. 
 Question: What metrics are used in challenge?

Ex Output:

NDCG MRR recall@k mean rank