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.

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.
What does recurrent deep stacking network do?

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.
Which dataset do they use for text modelling?

Very recently, the success of deep neural networks in many natural language processing tasks ( BIBREF20 ) has inspired new work in abstractive summarization . BIBREF2 propose a neural attention model with a convolutional encoder to solve this task.  More recently, BIBREF4 extended BIBREF2 's work with an RNN decoder, and BIBREF8 proposed an RNN encoder-decoder architecture for summarization. Both techniques are currently the state-of-the-art on the DUC competition.

What is the state-of-the art?

