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.

We present here a step in this direction: a probabilistic semantic parser that uses a large knowledge base (NELL) to form a prior probability distribution on the meanings of sentences it parses, and that "understands" each sentence either by identifying its existing beliefs that correspond to the sentence's meaning, or by creating new beliefs.
What knowledge bases do they use?

To the best of our knowledge, the only scientific summarization benchmark is from TAC 2014 summarization track. For evaluating the effectiveness of Rouge variants and our metric (Sera), we use this benchmark, which consists of 20 topics each with a biomedical journal article and 4 gold human written summaries. Consider the following example:

Endogeneous small RNAs (miRNA) were genetically screened and studied to find the miRNAs which are related to tumorigenesis.
Do the authors report results only on English data?

Argus Dataset AI2-8grade/CK12 Dataset We consider this dataset as preliminary since it was not reviewed by a human and many hypotheses are apparently unprovable by the evidence we have gathered (i.e. the theoretical top accuracy is much lower than 1.0). MCTest Dataset We use an official extension of the dataset for RTE evaluation that again textually merges questions and answers.
what datasets did they use?