# Efficient Summarization with Read-Again and Copy Mechanism

arXiv: https://arxiv.org/abs/1611.03382 · HTML: https://mlsanity.com/paper/1611.03382/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2016.

## Built on by (most cited first, 6 of 6)

- [An Extractive-and-Abstractive Framework for Source Code Summarization](https://mlsanity.com/paper/2206.07245/) (2022)
- [Explanation as a Defense of Recommendation](https://mlsanity.com/paper/2101.09656/) (2021)
- [Enhance Long Text Understanding via Distilled Gist Detector from Abstractive Summarization](https://mlsanity.com/paper/2110.04741/) (2021)
- [Reinforced Generative Adversarial Network for Abstractive Text Summarization](https://mlsanity.com/paper/2105.15176/) (2021)
- [Sparse Optimization for Unsupervised Extractive Summarization of Long Documents with the Frank-Wolfe Algorithm](https://mlsanity.com/paper/2208.09454/) (2022)
- [A Split-then-Join Approach to Abstractive Summarization for Very Long Documents in a Low Resource Setting](https://mlsanity.com/paper/2505.06862/) (2025)

## Abstract (arXiv)

Encoder-decoder models have been widely used to solve sequence to sequence prediction tasks. However current approaches suffer from two shortcomings. First, the encoders compute a representation of each word taking into account only the history of the words it has read so far, yielding suboptimal representations. Second, current decoders utilize large vocabularies in order to minimize the problem of unknown words, resulting in slow decoding times. In this paper we address both shortcomings. Towards this goal, we first introduce a simple mechanism that first reads the input sequence before committing to a representation of each word. Furthermore, we propose a simple copy mechanism that is able to exploit very small vocabularies and handle out-of-vocabulary words. We demonstrate the effectiveness of our approach on the Gigaword dataset and DUC competition outperforming the state-of-the-art.