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.

The task is set up to mimic (albeit, in an oversimplified manner) the input-output symbol alignments and local syntactic properties that models must learn in many natural language tasks, such as translation, tagging and summarization. 
 Question: Why does the proposed task a good proxy for the general-purpose sequence to sequence tasks?
The task is set up to mimic (albeit, in an oversimplified manner) the input-output symbol alignments and local syntactic properties that models must learn in many natural language tasks, such as translation, tagging and summarization.

The dataset consists of 198,112 news articles. 
 Question: How many articles did they have?
198,112

In addition, we include state-of-the-art models : RegSum BIBREF0 and GCN+PADG BIBREF3. We outperform both in terms of ROUGE-1. For ROUGE-2 scores we achieve better results than GCN+PADG but without any use of domain-specific hand-crafted features and a much smaller and simpler model. Finally, RegSum achieves a similar ROUGE-2 score but computes sentence saliences based on word scores, incorporating a rich set of word-level and domain-specific features. 
 Question: How better are state-of-the-art results than this model? 

we achieve better results than GCN+PADG but without any use of domain-specific hand-crafted features  RegSum achieves a similar ROUGE-2 score