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.

[Q]: We perform experiments using the following state-of-the-art models: (1) SEQ2SEQ: a sequence-to-sequence model with attention mechanisms BIBREF21, (2) CVAE: a conditional variational auto-encoder model with KL-annealing and a BOW loss BIBREF2, (3) Transformer: an encoder-decoder architecture relying solely on attention mechanisms BIBREF22, (4) HRED: a generalized sequence-to-sequence model with the hierarchical RNN encoder BIBREF23, (5) DialogWAE: a conditional Wasserstein auto-encoder, which models the distribution of data by training a GAN within the latent variable space BIBREF6.
[A]: What state of the art models were used in experiments?


[Q]: To discover topics from the collected tweets, we used a topic modeling approach that fuzzy clusters the semantically related words such as assigning “diabetes", “cancer", and “influenza" into a topic that has an overall “disease" theme BIBREF44 , BIBREF45 . Among topic models, Latent Dirichlet Allocation (LDA) BIBREF49 is the most popular effective model BIBREF50 , BIBREF19 as studies have shown that LDA is an effective computational linguistics model for discovering topics in a corpus BIBREF51 , BIBREF52 . We used the Mallet implementation of LDA BIBREF49 , BIBREF56 with its default settings to explore opinions in the tweets.
[A]: How were topics of interest about DDEO identified?


[Q]: To compare with our model, we select a number of strong summarization models as baseline systems. $\textsc {Lead-X}$ uses the top $X$ sentences as a summary BIBREF19. $\textsc {DRM}$ BIBREF10 leverages deep reinforcement learning for summarization. $\textsc {TConvS2S}$ BIBREF2 is based on convolutional neural networks. $\textsc {BottomUp}$ 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.
[A]:
What were the baselines?