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.

Ex Input:
 On high level, we indirectly evaluate the content relevance between the candidate summary and the human summary using information retrieval. To accomplish this, we use the summaries as search queries and compare the overlaps of the retrieved results.  
 Question: In the proposed metric, how is content relevance measured?

Ex Output:
Abstractive


Ex Input:
The decoder is the second LSTM network that uses the information obtained from the encoder to generate the sequence's story. The first input $x_0$ to the decoder is the image for which the text is being generated. The last hidden state from the encoder $h_e^{(t)}$ is used to initialize the first hidden state of the decoder $h_d^{(0)}$ . With this strategy, we provide the decoder with the context of the whole sequence and the content of the current image (i.e. global and local information) to generate the corresponding text that will contribute to the overall story. 
 Question: How is the sequential nature of the story captured?

Ex Output:
Extractive


Ex Input:
As a first baseline we use Bag-of-Words, a well-known and robust text representations used in various domains BIBREF21 , combined with a standard shallow classifier, namely, a Support Vector Machine with linear kernel. We used LIBSVM implementation of SVM. 
 Question: Which shallow approaches did they experiment with?

Ex Output:

Extractive