TASK DEFINITION: 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.
PROBLEM: We introduce our proposed diversity, density, and homogeneity metrics with their detailed formulations and key intuitions. 
 Question: Did they propose other metrics?

SOLUTION: Yes-no

PROBLEM: Features. We argue that different kinds of features like the sentiment of the text, morality, and other text-based features are critical to detect the nonfactual Twitter accounts by utilizing their occurrence during reporting the news in an account's timeline. We employ a rich set of features borrowed from previous works in fake news, bias, and rumors detection BIBREF0, BIBREF1, BIBREF8, BIBREF9.

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Emotion: We build an emotions vector using word occurrences of 15 emotion types from two available emotional lexicons. We use the NRC lexicon BIBREF10, which contains $\sim $14K words labeled using the eight Plutchik's emotions BIBREF11. The other lexicon is SentiSense BIBREF12 which is a concept-based affective lexicon that attaches emotional meanings to concepts from the WordNet lexical database. It has $\sim $5.5 words labeled with emotions from a set of 14 emotional categories We use the categories that do not exist in the NRC lexicon.

Sentiment: We extract the sentiment of the tweets by employing EffectWordNet BIBREF13, SenticNet BIBREF14, NRC BIBREF10, and subj_lexicon BIBREF15, where each has the two sentiment classes, positive and negative.

Morality: Features based on morality foundation theory BIBREF16 where words are labeled in one of the following 10 categories (care, harm, fairness, cheating, loyalty, betrayal, authority, subversion, sanctity, and degradation).

Style: We use canonical stylistic features, such as the count of question marks, exclamation marks, consecutive characters and letters, links, hashtags, users' mentions. In addition, we extract the uppercase ratio and the tweet length.

Words embeddings: We extract words embeddings of the words of the tweet using $Glove\-840B-300d$ BIBREF17 pretrained model. The tweet final representation is obtained by averaging its words embeddings. 
 Question: What features are extracted?

SOLUTION: Extractive

PROBLEM: We work with a dataset composed of 3,206 news articles, each labeled real or fake, with a perfect 50/50 split between 1,603 real and fake articles, respectively. Fake articles were sourced from online sites that were tagged as fake news sites by the non-profit independent media fact-checking organization Verafiles and the National Union of Journalists in the Philippines (NUJP). Real articles were sourced from mainstream news websites in the Philippines, including Pilipino Star Ngayon, Abante, and Bandera. 
 Question: What is the source of the dataset?

SOLUTION:
Abstractive