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: In order to achieve these configuration the TIMIT data was split. Fig. FIGREF12 illustrates the split of the data into 8 subsets (A-H). The TIMIT dataset contains speech from 462 speakers in training and 168 speakers in the test set, with 8 utterances for each speaker. The TIMIT training and test set are split into 8 blocks, where each block contains 2 utterances per speaker, randomly chosen. Thus each block A,B,C,D contains data from 462 speakers with 924 utterances taken from the training sets, and each block E,F,G,H contains speech from 168 test set speakers with 336 utterances.

For Task a training of embeddings and the classifier is identical, namely consisting of data from blocks (A+B+C+E+F+G). The test data is the remainder, namely blocks (D+H). For Task b the training of embeddings and classifiers uses (A+B+E+F) and (C+G) respectively, while again using (D+H) for test. Task c keeps both separate: embeddings are trained on (A+B+C+D), classifiers on (E+G) and tests are conducted on (F+H). Note that H is part of all tasks, and that Task c is considerably easier as the number of speakers to separate is only 168, although training conditions are more difficult. 
 Question: What TIMIT datasets are used for testing?

A: Abstractive
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Q: As explained in Section SECREF15 , the corruption introduced in Doc2VecC acts as a data-dependent regularization that suppresses the embeddings of frequent but uninformative words. In contrast, Doc2VecC manages to clamp down the representation of words frequently appear in the training set, but are uninformative, such as symbols and stop words. 
 Question: How do they determine which words are informative?

A: Abstractive
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Q: However, the coherence of the topics produced by LDA is poorer than expected.

To address this lack of coherence, we applied non-negative matrix factorization (NMF). 
 Question: How are prominent topics idenified in Dabiq and Rumiyah?

A:
Extractive
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