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 recent years, researchers have proposed various filter based feature selection methods to raise the performance of document text classification BIBREF19.  Furthermore, in order to rank the terms based on their discriminative power among the classes, we use filter based feature selection method named as Normalized Difference Measure (NDM)BIBREF5. Considering the features contour plot, Rehman et al. BIBREF5 suggested that all those features which exist in top left, and bottom right corners of the contour are extremely significant as compared to those features which exist around diagonals. State-of-the-art filter based feature selection algorithms such as ACC2 treat all those features in the same fashion which exist around the diagonals BIBREF5. 
 Question: Is the filter based feature selection (FSE) a form of regularization?
[A]: Yes-no


[Q]: There are three main components to the model: 1) input encoder 2) dynamic memory, and 3) output module. We will describe these three modules in details. The input encoder and output module implementations are similar to the Entity Network BIBREF17 and main novelty lies in the dynamic memory. We describe the operations executed by the network for a single example consisting of a document with $T$ sentences, where each sentence consists of a sequence of words represented with $K$ -dimensional word embeddings $\lbrace e_1, \ldots , e_N\rbrace $ , a question on the document represented as another sequence of words and an answer to the question. 
 Question: How is knowledge stored in the memory?
[A]: Abstractive


[Q]: . While automated evaluation metrics like ROUGE measure lexical similarity between machine and human summaries, humans can better measure how coherent and readable a summary is. Our evaluation study investigates whether tuning the PG-net model increases summary coherence, by asking evaluators to select which of three summaries for the same document they like most: the PG-net model trained on CNN/DM; the model trained on student reflections; and finally the model trained on CNN/DM and tuned on student reflections. 20 evaluators were recruited from our institution and asked to each perform 20 annotations. Summaries are presented to evaluators in random order. Evaluators are then asked to select the summary they feel to be most readable and coherent. 
 Question: Who were the human evaluators used?
[A]:
Extractive


