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.

[Q]: This paper used the real-time method to randomly collect 10% of publicly available English tweets using several pre-defined DDEO-related queries (Table TABREF6 ) within a specific time frame.  
 Question: Do they evaluate only on English data?
[A]: Yes-no


[Q]: In this section, we evaluate the proposed method intrinsically in terms of whether the co-occurrence matrix after the low-rank approximation is able to capture similar concepts on student response data sets, and also extrinsically in terms of the end task of summarization on all corpora. In the following experiments, summary length is set to be the average number of words in human summaries or less. An alternative way to evaluate the hypothesis is to let humans judge whether two bigrams are similar or not, which we leave for future work. 
 Question: Do they quantitavely or qualitatively evalute the output of their low-rank approximation to verify the grouping of lexical items?
[A]: Abstractive


[Q]: TF-IDF: We applied TF-IDF features as inputs to four classifiers: support vector machine classifier (SVM), logistic regression classifier (LR), Naive Bayes classifier (NB) and random forest (RF); CNN: We followed the same architecture as BIBREF9; LSTM: We applied an LSTM model BIBREF20 to classify the sentences with pre-trained word embeddings;LSTM-soft: We then added a soft-attention BIBREF21 layer on top of the LSTM model where we computed soft alignment scores over each of the hidden states; LSTM-self: We applied a self-attention layer BIBREF22 to LSTM model.  
 Question: To what baseline models is proposed model compared?
[A]:
Extractive