  In this work, we introduce temporal hierarchies to the sequence to sequence
(seq2seq) model to tackle the problem of abstractive summarization of
scientific articles. The proposed Multiple Timescale model of the Gated
Recurrent Unit (MTGRU) is implemented in the encoder-decoder setting to better
deal with the presence of multiple compositionalities in larger texts. The
proposed model is compared to the conventional RNN encoder-decoder, and the
results demonstrate that our model trains faster and shows significant
performance gains. The results also show that the temporal hierarchies help
improve the ability of seq2seq models to capture compositionalities better
without the presence of highly complex architectural hierarchies.
