# Controlling Output Length in Neural Encoder-Decoders

arXiv: https://arxiv.org/abs/1609.09552 · HTML: https://mlsanity.com/paper/1609.09552/

## Key facts

- Cited by 75 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2016.

## Built on by (most cited first, 30 of 75)

- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [A Long Way to Go: Investigating Length Correlations in RLHF](https://mlsanity.com/paper/2310.03716/) (2023)
- [Aspect-Controllable Opinion Summarization](https://mlsanity.com/paper/2109.03171/) (2021)
- [Controlling Hallucinations at Word Level in Data-to-Text Generation](https://mlsanity.com/paper/2102.02810/) (2021)
- [Data-to-text Generation with Macro Planning](https://mlsanity.com/paper/2102.02723/) (2021)
- [Controllable Abstractive Dialogue Summarization with Sketch Supervision](https://mlsanity.com/paper/2105.14064/) (2021)
- [Control Prefixes for Parameter-Efficient Text Generation](https://mlsanity.com/paper/2110.08329/) (2021)
- [EntSUM: A Data Set for Entity-Centric Summarization](https://mlsanity.com/paper/2204.02213/) (2022)
- [Improving Pacing in Long-Form Story Planning](https://mlsanity.com/paper/2311.04459/) (2023)
- [Why is constrained neural language generation particularly challenging?](https://mlsanity.com/paper/2206.05395/) (2022)
- [Deep Extrapolation for Attribute-Enhanced Generation](https://mlsanity.com/paper/2107.02968/) (2021)
- [Machine Translation Verbosity Control for Automatic Dubbing](https://mlsanity.com/paper/2110.03847/) (2021)
- [Controllable Text Summarization: Unraveling Challenges, Approaches, and Prospects -- A Survey](https://mlsanity.com/paper/2311.09212/) (2023)
- [Controllable Summarization with Constrained Markov Decision Process](https://mlsanity.com/paper/2108.03405/) (2021)
- ["Why is this misleading?": Detecting News Headline Hallucinations with Explanations](https://mlsanity.com/paper/2302.05852/) (2023)
- [From Chaos to Clarity: Claim Normalization to Empower Fact-Checking](https://mlsanity.com/paper/2310.14338/) (2023)
- [LenAtten: An Effective Length Controlling Unit For Text Summarization](https://mlsanity.com/paper/2106.00316/) (2021)
- [Referee: Reference-Free Sentence Summarization with Sharper Controllability through Symbolic Knowledge Distillation](https://mlsanity.com/paper/2210.13800/) (2022)
- [Text Compression-aided Transformer Encoding](https://mlsanity.com/paper/2102.05951/) (2021)
- [A New Approach to Overgenerating and Scoring Abstractive Summaries](https://mlsanity.com/paper/2104.01726/) (2021)
- [Style Pooling: Automatic Text Style Obfuscation for Improved Classification Fairness](https://mlsanity.com/paper/2109.04624/) (2021)
- [Context-Tuning: Learning Contextualized Prompts for Natural Language Generation](https://mlsanity.com/paper/2201.08670/) (2022)
- [Learning Combinatorial Prompts for Universal Controllable Image Captioning](https://mlsanity.com/paper/2303.06338/) (2023)
- [WS-GRPO: Weakly-Supervised Group-Relative Policy Optimization for Rollout-Efficient Reasoning](https://mlsanity.com/paper/2602.17025/) (2026)
- [Lost in Interpreting: Speech Translation from Source or Interpreter?](https://mlsanity.com/paper/2106.09343/) (2021)
- [Style Control for Schema-Guided Natural Language Generation](https://mlsanity.com/paper/2109.12211/) (2021)
- [Multi-modal Segment Assemblage Network for Ad Video Editing with Importance-Coherence Reward](https://mlsanity.com/paper/2209.12164/) (2022)
- [Time-aware Prompting for Text Generation](https://mlsanity.com/paper/2211.02162/) (2022)
- [Zero-Shot Strategies for Length-Controllable Summarization](https://mlsanity.com/paper/2501.00233/) (2024)
- [ConInstruct: Evaluating Large Language Models on Conflict Detection and Resolution in Instructions](https://mlsanity.com/paper/2511.14342/) (2025)

## Abstract (arXiv)

Neural encoder-decoder models have shown great success in many sequence generation tasks. However, previous work has not investigated situations in which we would like to control the length of encoder-decoder outputs. This capability is crucial for applications such as text summarization, in which we have to generate concise summaries with a desired length. In this paper, we propose methods for controlling the output sequence length for neural encoder-decoder models: two decoding-based methods and two learning-based methods. Results show that our learning-based methods have the capability to control length without degrading summary quality in a summarization task.