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.

[Q]: There are 8757 news records in our preprocessed data set. 
[A]: How large is the dataset?


[Q]: We designed 3 evaluation sets: (1) Base Set (1,264 samples) held out from the simulated data. (2) Augmented Set (1,280 samples) built by adding two out-of-distribution symptoms, with corresponding dialogue contents and queries, to the Base Set (“bleeding” and “cold”, which never appeared in training data). (3) Real-World Set (944 samples) manually delineated from the the symptom checking portions (approximately 4 hours) of real-world dialogues, and annotated as evaluation samples.
[A]: How do they select instances to their hold-out test set?


[Q]: Following lebret2016neural, we used BLEU-4, NIST-4 and ROUGE-4 as the evaluation metrics.
[A]:
What metrics are used for evaluation?