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

arXiv: https://arxiv.org/abs/1607.00718 · HTML: https://mlsanity.com/paper/1607.00718/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2016.

## Built on by (most cited first, 3 of 3)

- [Generation of Highlights from Research Papers Using Pointer-Generator Networks and SciBERT Embeddings](https://mlsanity.com/paper/2302.07729/) (2023)
- [Named Entity Recognition Based Automatic Generation of Research Highlights](https://mlsanity.com/paper/2303.12795/) (2023)
- [SimCSum: Joint Learning of Simplification and Cross-lingual Summarization for Cross-lingual Science Journalism](https://mlsanity.com/paper/2304.01621/) (2023)

## Abstract (arXiv)

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.