# A Character-Level Length-Control Algorithm for Non-Autoregressive Sentence Summarization

arXiv: https://arxiv.org/abs/2205.14522 · HTML: https://mlsanity.com/paper/2205.14522/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 51 references that are in the map.
- First posted on arXiv 28 May 2022.

## Builds on (most cited first, 28 of 28)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [Non-Autoregressive Neural Machine Translation](https://mlsanity.com/paper/1711.02281/) (2017)
- [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)
- [Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement](https://mlsanity.com/paper/1802.06901/) (2018)
- [Bottom-Up Abstractive Summarization](https://mlsanity.com/paper/1808.10792/) (2018)
- [Non-Autoregressive Machine Translation with Latent Alignments](https://mlsanity.com/paper/2004.07437/) (2020)
- [CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling](https://mlsanity.com/paper/1811.10996/) (2018)
- [Fast Structured Decoding for Sequence Models](https://mlsanity.com/paper/1910.11555/) (2019)
- [Positional Encoding to Control Output Sequence Length](https://mlsanity.com/paper/1904.07418/) (2019)
- [BanditSum: Extractive Summarization as a Contextual Bandit](https://mlsanity.com/paper/1809.09672/) (2018)
- [Non-Autoregressive Translation with Layer-Wise Prediction and Deep Supervision](https://mlsanity.com/paper/2110.07515/) (2021)
- [BottleSum: Unsupervised and Self-supervised Sentence Summarization using the Information Bottleneck Principle](https://mlsanity.com/paper/1909.07405/) (2019)
- [Neural Extractive Text Summarization with Syntactic Compression](https://mlsanity.com/paper/1902.00863/) (2019)
- [Align-Refine: Non-Autoregressive Speech Recognition via Iterative Realignment](https://mlsanity.com/paper/2010.14233/) (2020)
- [Non-Autoregressive Text Generation with Pre-trained Language Models](https://mlsanity.com/paper/2102.08220/) (2021)
- [BANG: Bridging Autoregressive and Non-autoregressive Generation with Large Scale Pretraining](https://mlsanity.com/paper/2012.15525/) (2020)
- [TED: A Pretrained Unsupervised Summarization Model with Theme Modeling and Denoising](https://mlsanity.com/paper/2001.00725/) (2020)
- [SEQ^3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression](https://mlsanity.com/paper/1904.03651/) (2019)
- [RefSum: Refactoring Neural Summarization](https://mlsanity.com/paper/2104.07210/) (2021)
- [SummPip: Unsupervised Multi-Document Summarization with Sentence Graph Compression](https://mlsanity.com/paper/2007.08954/) (2020)
- [Discrete Optimization for Unsupervised Sentence Summarization with Word-Level Extraction](https://mlsanity.com/paper/2005.01791/) (2020)
- [Jointly Extracting and Compressing Documents with Summary State Representations](https://mlsanity.com/paper/1904.02020/) (2019)
- [Learning to Encode Text as Human-Readable Summaries using Generative Adversarial Networks](https://mlsanity.com/paper/1810.02851/) (2018)
- [Length-controllable Abstractive Summarization by Guiding with Summary Prototype](https://mlsanity.com/paper/2001.07331/) (2020)
- [Cascaded Text Generation with Markov Transformers](https://mlsanity.com/paper/2006.01112/) (2020)

## Built on by (most cited first, 13 of 13)

- [Counting Ability of Large Language Models and Impact of Tokenization](https://mlsanity.com/paper/2410.19730/) (2024)
- [Why Prompt Design Matters and Works: A Complexity Analysis of Prompt Search Space in LLMs](https://mlsanity.com/paper/2503.10084/) (2025)
- [TimeSeriesScientist: A General-Purpose AI Agent for Time Series Analysis](https://mlsanity.com/paper/2510.01538/) (2025)
- [Controllable Text Summarization: Unraveling Challenges, Approaches, and Prospects -- A Survey](https://mlsanity.com/paper/2311.09212/) (2023)
- [Multi2: Multi-Agent Test-Time Scalable Framework for Multi-Document Processing](https://mlsanity.com/paper/2502.20592/) (2025)
- [PosterGen: Aesthetic-Aware Multi-Modal Paper-to-Poster Generation via Multi-Agent LLMs](https://mlsanity.com/paper/2508.17188/) (2025)
- [Supervised Chain of Thought](https://mlsanity.com/paper/2410.14198/) (2024)
- [Universal Biological Sequence Reranking for Improved De Novo Peptide Sequencing](https://mlsanity.com/paper/2505.17552/) (2025)
- [Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing](https://mlsanity.com/paper/2506.13485/) (2025)
- [Multilingual Non-Autoregressive Machine Translation without Knowledge Distillation](https://mlsanity.com/paper/2502.04537/) (2025)
- [Bidirectional Representations Augmented Autoregressive Biological Sequence Generation](https://mlsanity.com/paper/2510.08169/) (2025)
- [From Lengthy to Lucid: A Systematic Literature Review on NLP Techniques for Taming Long Sentences](https://mlsanity.com/paper/2312.05172/) (2023)
- [Agent Memory with Episodic Retrieval for Financial Decision-Making](https://mlsanity.com/paper/2609.28771/) (2026)

## Abstract (arXiv)

Sentence summarization aims at compressing a long sentence into a short one that keeps the main gist, and has extensive real-world applications such as headline generation. In previous work, researchers have developed various approaches to improve the ROUGE score, which is the main evaluation metric for summarization, whereas controlling the summary length has not drawn much attention. In our work, we address a new problem of explicit character-level length control for summarization, and propose a dynamic programming algorithm based on the Connectionist Temporal Classification (CTC) model. Results show that our approach not only achieves higher ROUGE scores but also yields more complete sentences.