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 Q]: We use 300 Dimensional Glove Common Crawl Embeddings (840B Token) BIBREF11 and fine tune them for the task.
[EX A]: What data are the embeddings trained on?

[EX Q]: We introduce a new model for difficulty prediction combining learned representations induced via a pre-trained `universal' sentence encoder BIBREF6 , and a sentence encoder learned from scratch for this task. In total, this resulted in 57,505 and 2,428 sentences in the train and test set abstracts, respectively.
[EX A]: How much data is needed to train the task-specific encoder?

[EX Q]: In this step we extract the AMR graphs of the summary sentences using story sentence AMRs. We divide this task in two parts. First is finding the important sentences from the story and then extracting the key information from those sentences using their AMR graphs.
[EX A]:
How are sentences selected from the summary graph?