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.

[EX Q]: We evaluate our model on two public datasets, namely, Penn Treebank (PTB) BIBREF9 and the end-to-end (E2E) text generation corpus BIBREF10, which have been used in a number of previous works for text generation BIBREF0, BIBREF5, BIBREF11, BIBREF12. PTB consists of more than 40,000 sentences from Wall Street Journal articles whereas the E2E dataset contains over 50,000 sentences of restaurant reviews. The statistics of these two datasets are summarised in Table TABREF11.
[EX A]: Which dataset do they use for text modelling?

[EX Q]: In BIBREF0 , BIBREF1 , BIBREF2 , Pasca et al. firstly extract potential class-attribute pairs using linguistically motivated patterns from unstructured text including query logs and query sessions, and then score the attributes using the Bayes model. In BIBREF3 , Rahul Rai proposed to identify product attributes from customer online reviews using part-of-speech(POS) tagging patterns, and to evaluate their importance with several different frequency metrics. In BIBREF4 , Lee et al. developed a system to extract concept-attribute pairs from multiple data sources, such as Probase, general web documents, query logs and external knowledge base, and aggregate the weights from different sources into one consistent typicality score using a Ranking SVM model. In BIBREF5 , Li et al. introduced the OntoRank algorithm for ranking the importance of semantic web objects at three levels of granularity: document, terms and RDF graphs. The algorithm is based on the rational surfer model, successfully used in the Swoogle semantic web search engine.
[EX A]: What are the traditional methods to identifying important attributes?

[EX Q]: 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.
[EX A]:
How better are state-of-the-art results than this model?