# Reinforced Abstractive Summarization with Adaptive Length Controlling

arXiv: https://arxiv.org/abs/2112.07534 · HTML: https://mlsanity.com/paper/2112.07534/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 26 references that are in the map.
- First posted on arXiv 14 Dec 2021.

## Builds on (most cited first, 19 of 19)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [A Neural Attention Model for Abstractive Sentence Summarization](https://mlsanity.com/paper/1509.00685/) (2015)
- [A Deep Reinforced Model for Abstractive Summarization](https://mlsanity.com/paper/1705.04304/) (2017)
- [SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents](https://mlsanity.com/paper/1611.04230/) (2016)
- [Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting](https://mlsanity.com/paper/1805.11080/) (2018)
- [BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization](https://mlsanity.com/paper/1906.03741/) (2019)
- [Ranking Sentences for Extractive Summarization with Reinforcement Learning](https://mlsanity.com/paper/1802.08636/) (2018)
- [Neural Summarization by Extracting Sentences and Words](https://mlsanity.com/paper/1603.07252/) (2016)
- [Controllable Abstractive Summarization](https://mlsanity.com/paper/1711.05217/) (2017)
- [Controlling Output Length in Neural Encoder-Decoders](https://mlsanity.com/paper/1609.09552/) (2016)
- [Multi-Reward Reinforced Summarization with Saliency and Entailment](https://mlsanity.com/paper/1804.06451/) (2018)
- [An Entity-Driven Framework for Abstractive Summarization](https://mlsanity.com/paper/1909.02059/) (2019)
- [Summary Level Training of Sentence Rewriting for Abstractive Summarization](https://mlsanity.com/paper/1909.08752/) (2019)
- [Length-controllable Abstractive Summarization by Guiding with Summary Prototype](https://mlsanity.com/paper/2001.07331/) (2020)
- [An Editorial Network for Enhanced Document Summarization](https://mlsanity.com/paper/1902.10360/) (2019)
- [Controllable Length Control Neural Encoder-Decoder via Reinforcement Learning](https://mlsanity.com/paper/1909.09492/) (2019)

## Abstract (arXiv)

Document summarization, as a fundamental task in natural language generation, aims to generate a short and coherent summary for a given document. Controllable summarization, especially of the length, is an important issue for some practical applications, especially how to trade-off the length constraint and information integrity. In this paper, we propose an Adaptive Length Controlling Optimization (ALCO) method to leverage two-stage abstractive summarization model via reinforcement learning. ALCO incorporates length constraint into the stage of sentence extraction to penalize the overlength extracted sentences. Meanwhile, a saliency estimation mechanism is designed to preserve the salient information in the generated sentences. A series of experiments have been conducted on a wildly-used benchmark dataset textitCNN/Daily Mail. The results have shown that ALCO performs better than the popular baselines in terms of length controllability and content preservation.