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 propose a natural language generation model based on BERT, making good use of the pre-trained language model in the encoder and decoder process, and the model can be trained end-to-end without handcrafted features. During model training, the objective of our model is sum of the two processes, jointly trained using "teacher-forcing" algorithm. During training we feed the ground-truth summary to each decoder and minimize the objective.

$$L_{model} = \hat{L}_{dec} + \hat{L}_{refine}$$ (Eq. 23)

At test time, each time step we choose the predicted word by $\hat{y} = argmax_{y^{\prime }} P(y^{\prime }|x)$ , use beam search to generate the draft summaries, and use greedy search to generate the refined summaries.
[A]: How are the different components of the model trained? Is it trained end-to-end?


[Q]: CIC is a suitable complementary metric to ROUGE because it accounts for the most important information within each dialog domain. CIC can be applied to any summarization task with predefined essential entities.
[A]: Is new evaluation metric extension of ROGUE?


[Q]: Note that the question answering tasks we consider here are multimodal in that while the context is a procedural text, the question and the multiple choice answers are composed of images.
[A]:
What multimodality is available in the dataset?