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

Answer: Yes-no


Question: Here we introduce the evaluation setup and analyze the results for the article-entity (AEP) placement task. We only report the evaluation metrics for the `relevant' news-entity pairs.  Baselines. We consider the following baselines for this task.

B1. The first baseline uses only the salience-based features by Dunietz and Gillick BIBREF11 .

B2. The second baseline assigns the value relevant to a pair INLINEFORM0 , if and only if INLINEFORM1 appears in the title of INLINEFORM2 . Here we show the evaluation setup for ASP task and discuss the results with a focus on three main aspects, (i) the overall performance across the years, (ii) the entity class specific performance, and (iii) the impact on entity profile expansion by suggesting missing sections to entities based on the pre-computed templates. Baselines. To the best of our knowledge, we are not aware of any comparable approach for this task. Therefore, the baselines we consider are the following:

S1: Pick the section from template INLINEFORM0 with the highest lexical similarity to INLINEFORM1 : S1 INLINEFORM2

S2: Place the news into the most frequent section in INLINEFORM0 
 Question: What baseline model is used?

Answer: Abstractive


Question: We used the relation classification dataset of the SemEval 2010 task 8 BIBREF8 . It consists of sentences which have been manually labeled with 19 relations (9 directed relations and one artificial class Other). 8,000 sentences have been distributed as training set and 2,717 sentences served as test set. 
 Question: Which dataset do they train their models on?

Answer:
Extractive