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.

Results reveal that, contrary to the common belief, correlations between Rouge and the Pyramid scores are weak, which challenges its effectiveness for scientific summarization. Furthermore, we show a large variance of correlations between different Rouge variants and the manual evaluations which further makes the reliability of Rouge for evaluating scientific summaries less clear. 
 Question: What is the common belief that this paper refutes? (c.f. 'contrary to the common belief, ROUGE is not much [sic] reliable'
Extractive

Table TABREF14 shows the effectiveness of our model (multi-task transformer) over the baseline transformer BIBREF8 .  
 Question: what are the baselines?
Extractive

 Table 1 shows the results of all models on WikiLarge dataset. We can see that our method (NMT+synthetic) can obtain higher BLEU, lower FKGL and high SARI compared with other models, except Dress on FKGL and SBMT-SARI on SARI. It verified that including synthetic data during training is very effective, and yields an improvement over our baseline NMF by 2.11 BLEU, 1.7 FKGL and 1.07 SARI.  Results on WikiSmall dataset are shown in Table 2. We see substantial improvements (6.37 BLEU) than NMT from adding simplified training data with synthetic ordinary sentences.  
 Question: by how much did their model improve?

Abstractive