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:
We use two datasets for experiments (Table TABREF29 ): (a) STAN INLINEFORM0 , created by BIBREF10 BansalBV15, which consists of 1,108 unique English hashtags from 1,268 randomly selected tweets in the Stanford Sentiment Analysis Dataset BIBREF36 along with their crowdsourced segmentations and our additional corrections; and (b) STAN INLINEFORM1 , our new expert curated dataset, which includes all 12,594 unique English hashtags and their associated tweets from the same Stanford dataset. 
 Question: How is the dataset of hashtags sourced?

Ex Output:
Extractive


Ex Input:
Second, we compute three metrics on the extracted information:

$\bullet $ Relation Generation (RG) estimates how well the system is able to generate text containing factual (i.e., correct) records. We measure the precision and absolute number (denoted respectively RG-P% and RG-#) of unique relations $r$ extracted from $\hat{y}_{1:T}$ that also appear in $s$.

$\bullet $ Content Selection (CS) measures how well the generated document matches the gold document in terms of mentioned records. We measure the precision and recall (denoted respectively CS-P% and CS-R%) of unique relations $r$ extracted from $\hat{y}_{1:T}$ that are also extracted from $y_{1:T}$.

$\bullet $ Content Ordering (CO) analyzes how well the system orders the records discussed in the description. We measure the normalized Damerau-Levenshtein distance BIBREF36 between the sequences of records extracted from $\hat{y}_{1:T}$ that are also extracted from $y_{1:T}$. 
 Question: Which qualitative metric are used for evaluation?

Ex Output:
Extractive


Ex Input:
Two datasets are exploited in this article. Both datasets consist of plain text containing clinical narrative written in Spanish, and their respective manual annotations of sensitive information in BRAT BIBREF13 standoff format. NUBes BIBREF4 is a corpus of around 7,000 real medical reports written in Spanish and annotated with negation and uncertainty information. In order to avoid confusion between the two corpus versions, we henceforth refer to the version relevant in this paper as NUBes-PHI (from `NUBes with Personal Health Information'). The organisers of the MEDDOCAN shared task BIBREF3 curated a synthetic corpus of clinical cases enriched with sensitive information by health documentalists 
 Question: What are the clinical datasets used in the paper?

Ex Output:

Extractive