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.

Example Input: Consequently, we investigate ways to detect suspicious accounts by considering their tweets in groups (chunks). Our hypothesis is that suspicious accounts have a unique pattern in posting tweet sequences. Since their intention is to mislead, the way they transition from one set of tweets to the next has a hidden signature, biased by their intentions. Given a news Twitter account, we read its tweets from the account's timeline. Then we sort the tweets by the posting date in ascending way and we split them into $N$ chunks. Each chunk consists of a sorted sequence of tweets labeled by the label of its corresponding account. 
 Question: How is a "chunk of posts" defined in this work?
Example Output: chunk consists of a sorted sequence of tweets labeled by the label of its corresponding account

Example Input: We use datasets from the NIST DUC-05, DUC-06 and DUC-07 shared tasks BIBREF7, BIBREF19, BIBREF20. Given a question and a cluster of newswire documents, the contestants were asked to generate a 250-word summary answering the question. DUC-05 contains 1,600 summaries (50 questions x 32 systems); in DUC-06, 1,750 summaries are included (50 questions x 35 systems); and DUC-07 has 1,440 summaries (45 questions x 32 systems). 
 Question: What dataset do they use?
Example Output: datasets from the NIST DUC-05, DUC-06 and DUC-07 shared tasks

Example Input: Data was collected from a 10% uniform sample of Twitter posts made during 2013, specifically the Gardenhose API. Control documents were also selected. These documents did not contain any of `caused', `causing', or `causes', nor any bidirectional words, and are further matched temporally to obtain the same number of control documents as causal documents in each fifteen-minute period during 2013. Control documents were otherwise selected randomly; causal synonyms may be present.  
 Question: How do they collect the control corpus?
Example Output:
Randomly from Twitter

