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 you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

To compare with our model, we select a number of strong summarization models as baseline systems. $ extsc {Lead-X}$ uses the top $X$ sentences as a summary BIBREF19. $ extsc {DRM}$ BIBREF10 leverages deep reinforcement learning for summarization. $ extsc {TConvS2S}$ BIBREF2 is based on convolutional neural networks. $ extsc {BottomUp}$ BIBREF11 uses a bottom-up approach to generate summarization. ABS BIBREF26 uses neural attention for summary generation. DRGD BIBREF27 is based on a deep recurrent generative decoder. To compare with our pretrain-only model, we include several unsupervised abstractive baselines: SEQ$^3$ BIBREF28 employs the reconstruction loss and topic loss for summarization. BottleSum BIBREF23 leverages unsupervised extractive and self-supervised abstractive methods. GPT-2 BIBREF7 is a large-scaled pretrained language model which can be directly used to generate summaries.
Output:
What were the baselines?