# Summarization with Precise Length Control

arXiv: https://arxiv.org/abs/2305.05171 · HTML: https://mlsanity.com/paper/2305.05171/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 15 references that are in the map.
- First posted on arXiv 9 May 2023.

## Builds on (most cited first, 12 of 12)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents](https://mlsanity.com/paper/1804.05685/) (2018)
- [Controllable Abstractive Summarization](https://mlsanity.com/paper/1711.05217/) (2017)
- [CTRLsum: Towards Generic Controllable Text Summarization](https://mlsanity.com/paper/2012.04281/) (2020)
- [Controlling Output Length in Neural Encoder-Decoders](https://mlsanity.com/paper/1609.09552/) (2016)
- [Positional Encoding to Control Output Sequence Length](https://mlsanity.com/paper/1904.07418/) (2019)
- [Z-Code++: A Pre-trained Language Model Optimized for Abstractive Summarization](https://mlsanity.com/paper/2208.09770/) (2022)
- [Controllable Summarization with Constrained Markov Decision Process](https://mlsanity.com/paper/2108.03405/) (2021)
- [LenAtten: An Effective Length Controlling Unit For Text Summarization](https://mlsanity.com/paper/2106.00316/) (2021)
- [Length-controllable Abstractive Summarization by Guiding with Summary Prototype](https://mlsanity.com/paper/2001.07331/) (2020)

## Built on by (most cited first, 4 of 4)

- [Improving Pacing in Long-Form Story Planning](https://mlsanity.com/paper/2311.04459/) (2023)
- [TransVIP: Speech to Speech Translation System with Voice and Isochrony Preservation](https://mlsanity.com/paper/2405.17809/) (2024)
- [Prompt-Based One-Shot Exact Length-Controlled Generation with LLMs](https://mlsanity.com/paper/2508.13805/) (2025)
- [Controlling Summarization Length Through EOS Token Weighting](https://mlsanity.com/paper/2506.05017/) (2025)

## Abstract (arXiv)

Many applications of text generation such as summarization benefit from accurately controlling the text length. Existing approaches on length-controlled summarization either result in degraded performance or can only control the length approximately. In this work, we present a framework to generate summaries with precisely the specified number of tokens or sentences, while maintaining or even improving the text quality. In addition, we jointly train the models to predict the lengths, so our model can generate summaries with optimal length. We evaluate the proposed framework on the CNNDM dataset and show improved performance compared to existing methods.