In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

Ex Input:
The retweeting behavior of MEPs is captured by their retweet network. Each MEP active on Twitter is a node in this network. An edge in the network between two MEPs exists when one MEP retweeted the other. The weight of the edge is the number of retweets between the two MEPs We measure the cohesion of a political group INLINEFORM0 as the average retweets, i.e., the ratio of the number of retweets between the MEPs in the group INLINEFORM1 to the number of MEPs in the group INLINEFORM2 

Ex Output:
Do they authors account for differences in usage of Twitter amongst MPs into their model?


Ex Input:
We compare our approach with two baseline vision-based methods proposed in BIBREF6 , BIBREF7 , which measure the similarity of two sets of global visual features for bilingual lexicon induction:

CNN-mean: taking the similarity score of the averaged feature of the two image sets.

CNN-avgmax: taking the average of the maximum similarity scores of two image sets.

Ex Output:
Which vision-based approaches does this approach outperform?


Ex Input:
The second series of tests consisted of using all the documents of the subcorpus “specialist” $E$, because the documents of the subcorpus of Annodis are not identical.  As far as system evaluations are concerned, we use the 78 $E$ documents as reference.  We calculate the precision $P$, the recall $R$ and the $F$-score on the text corpus used in our tests, as follow:

Ex Output:

How is segmentation quality evaluated?