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]: We compared several summarization methods which can be categorized into three groups: unsupervised, non-neural supervised, and neural supervised methods. As a baseline, we used Lead-N which selects INLINEFORM0 leading sentences as the summary. For all methods, we extracted 3 sentences as the summary since it is the median number of sentences in the gold summaries that we found in our exploratory analysis. Lead-3 baseline performs really well and outperforms almost all the other models, which is not surprising and even consistent with other work that for news summarization, Lead-N baseline is surprisingly hard to beat.
[A]: What was the best performing baseline?

[Q]: We pair 11'248 standard German written words with their phonetical representations in six different Swiss dialects: Zurich, St. Gallen, Basel, Bern, Visp, and Stans (Figure FIGREF1).
[A]: How many words are coded in the dictionary?

[Q]: We pre-trained our model on the Dutch section of the OSCAR corpus, a large multilingual corpus which was obtained by language classification in the Common Crawl corpus BIBREF16.
[A]:
What data did they use?