In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

[Q]: Experiments ::: Baselines
For comparison, we select several public models as baselines including semantic parsing models: BiDAF BIBREF3, an MRC model which utilizes a bi-directional attention flow network to encode the question and passage;

QANet BIBREF12, which utilizes convolutions and self-attentions as the building blocks of encoders to represent the question and passage;

BERT BIBREF23, a pre-trained bidirectional Transformer-based language model which achieves state-of-the-art performance on lots of public MRC datasets recently;

and numerical MRC models: NAQANet BIBREF6, a numerical version of QANet model.

NAQANet+, an enhanced version of NAQANet implemented by ourselves, which further considers real number (e.g. “2.5”), richer arithmetic expression, data augmentation, etc. Syn Dep BIBREF6, the neural semantic parsing model (KDG) BIBREF22 with Stanford dependencies based sentence representations;

OpenIE BIBREF6, KDG with open information extraction based sentence representations;

SRL BIBREF6, KDG with semantic role labeling based sentence representations;

and traditional MRC models: 
 Question: what are the existing models they compared with?
[A]: Syn Dep OpenIE SRL BiDAF QANet BERT NAQANet NAQANet+


[Q]: The summaries generated by our ILP-based approach were compared with the corresponding manual summaries using the ROUGE BIBREF22 unigram score. 
 Question: What evaluation metrics were used for the summarization task?
[A]: ROUGE BIBREF22 unigram score


[Q]: The DDI corpus contains thousands of XML files, each of which are constructed by several records. For a sentence containing INLINEFORM0 drugs, there are INLINEFORM1 drug pairs. 
 Question: How big is the evaluated dataset?
[A]:
contains thousands of XML files, each of which are constructed by several records