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.
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Question: But we also showed that in some cases the saliency maps seem to not capture the important input features. The second observation we can make is that the saliency map doesn't seem to highlight the right things in the input for the summary it generates

Answer: Is the explanation from saliency map correct?

Question: For each candidate mention span, we retrieve the top 10 entities according to the Freebase API. We then create a lattice in which the nodes correspond to mention-entity pairs, scored by their Freebase API scores, and the edges encode the fact that no joint assignment of entities to mentions can contain overlapping spans. We take the top 10 paths through the lattice as possible entity disambiguations. For each possibility, we generate $n$ -best paraphrases that contains the entity mention spans. In the end, this process creates a total of $10n$ paraphrases.

Answer: How many paraphrases are generated per question?

Question: The number of fake news that have a duplicate in the training dataset are 1018 whereas, the number of articles with genuine content that have a duplicate article in the training set is 322.

Answer:
what is the size of the dataset?