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In this work, we introduce temporal hi- erarchies to the sequence to sequence (seq2seq) model to tackle the problem of abstractive summarization of scientific ar- ticles. The proposed Multiple Timescale model of the Gated Recurrent Unit (MT- GRU) is implemented in the encoder- decoder setting to better deal with the presence of multiple compositionalities in larger texts. The proposed model is com- pared to the conventional RNN encoder- decoder, and the results demonstrate that our model trains faster and shows signifi- cant 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 architec- tural hierarchies.

Towards Abstraction from Extraction: Multiple Timescale Gated Recurrent Unit for Summarization