instruction:
In this task, you will be presented with a context from an academic paper and a question based on the context. You have to classify the questions into "Extractive", "Abstractive", or "Yes-no" questions. Extractive questions can be answered by concatenating extracts taken from a context into a summary while answering abstractive questions involves paraphrasing the context using novel sentences. Yes-no question is a question whose expected answer is one of two choices, one that affirms the question and one that denies the question. Typically, the choices are either yes or no.
question:
Based on the resulting 1.8 million lists of about 169,000 distinct user ids, we compute a topic model with INLINEFORM0 topics using Latent Dirichlet Allocation BIBREF3 . For each of the user ids, we extract the most probable topic from the inferred user id-topic distribution as cluster id. This results in a thematic cluster id for most of the user ids in our background corpus grouping together accounts such as American or German political actors, musicians, media websites or sports clubs (see Table TABREF17 ).  
 Question: What topic clusters are identified by LDA?
answer:
Abstractive


question:
Causal documents were chosen to contain one occurrence only of the exact unigrams: `caused', `causing', or `causes'. 
 Question: What are the selection criteria for "causal statements"?
answer:
Abstractive


question:
In future work we also intend to add these types of studies to the ERP predictions.

Discussion 
 Question: Which two pairs of ERPs from the literature benefit from joint training?
answer:

Abstractive