You will be given a definition of a task first, then some input of the task.
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.

Specifically, we group the models based on the objective function it optimizes. We believe this work can aid the understanding of the existing literature. We believe that the performance of these models is highly dependent on the objective function it optimizes - predicting adjacent word (within-tweet relationships), adjacent tweet (inter-tweet relationships), the tweet itself (autoencoder), modeling from structured resources like paraphrase databases and weak supervision.
Question: How do they encourage understanding of literature as part of their objective function?
Output:
Abstractive