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: As indicated in its name, Recurrent Deep Stacking Network stacks and concatenates the outputs of previous frames into the input features of the current frame.

A: What does recurrent deep stacking network do?
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Q: Conditional Random Fields
Conditional Random Fields (CRF) BIBREF10 are a standard approach when dealing with sequential data in the context of sequence labeling. BiLSTM-CRF
Prior to the emergence of deep neural language models, BiLSTM-CRF models BIBREF11 had achieved state-of-the-art results for the task of sequence labeling. Multi-Task Learning
Multi-Task Learning (MTL) BIBREF15 has become popular with the progress in deep learning. BioBERT
Deep neural language models have recently evolved to a successful method for representing text. In particular, Bidirectional Encoder Representations from Transformers (BERT) outperformed previous state-of-the-art methods by a large margin on various NLP tasks BIBREF17.

A: What baseline systems are proposed?
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Q: In contrast, in order to exploit section information, in this paper we propose to capture a distributed representation of both the global (the whole document) and the local context (e.g., the section/topic) when deciding if a sentence should be included in the summary

A:
What do they mean by global and local context?
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