TASK DEFINITION: In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
PROBLEM: Experimental Setup
Question: what english datasets were used?

SOLUTION: Answer with content missing: (Data section) Penn Treebank (PTB)

PROBLEM: Since our methodology does not use a seed list of offensive words, it is not biased by topic, target, or dialect. Using our methodology, we tagged 10,000 Arabic tweet dataset for offensiveness, where offensive tweets account for roughly 19% of the tweets. Further, we labeled tweets as vulgar or hate speech. To date, this is the largest available dataset, which we plan to make publicly available along with annotation guidelines. We use this dataset to characterize Arabic offensive language to ascertain the topics, dialects, and users' gender that are most associated with the use of offensive language.
Question: In what way is the offensive dataset not biased by topic, dialect or target?

SOLUTION: It does not use a seed list to gather tweets so the dataset does not skew to specific topics, dialect, targets.

PROBLEM: In this work, we introduce IndoSum, a new benchmark dataset for Indonesian text summarization, and evaluated several well-known extractive single-document summarization methods on the dataset. The dataset consists of online news articles and has almost 200 times more documents than the next largest one of the same domain BIBREF2 We used a dataset provided by Shortir, an Indonesian news aggregator and summarizer company. The dataset contains roughly 20K news articles.
Question: What is the size of the dataset?

SOLUTION:
20K