# LenAtten: An Effective Length Controlling Unit For Text Summarization

arXiv: https://arxiv.org/abs/2106.00316 · HTML: https://mlsanity.com/paper/2106.00316/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 22 references that are in the map.
- First posted on arXiv 1 Jun 2021.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [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)
- [MASS: Masked Sequence to Sequence Pre-training for Language Generation](https://mlsanity.com/paper/1905.02450/) (2019)
- [SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents](https://mlsanity.com/paper/1611.04230/) (2016)
- [Incorporating Copying Mechanism in Sequence-to-Sequence Learning](https://mlsanity.com/paper/1603.06393/) (2016)
- [Extractive Summarization as Text Matching](https://mlsanity.com/paper/2004.08795/) (2020)
- [Controllable Abstractive Summarization](https://mlsanity.com/paper/1711.05217/) (2017)
- [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)

## Built on by (most cited first, 14 of 14)

- [Prompt-Based Length Controlled Generation with Multiple Control Types](https://mlsanity.com/paper/2406.10278/) (2024)
- [Controllable Text Summarization: Unraveling Challenges, Approaches, and Prospects -- A Survey](https://mlsanity.com/paper/2311.09212/) (2023)
- [Prompt-Based Length Controlled Generation with Reinforcement Learning](https://mlsanity.com/paper/2308.12030/) (2023)
- [Zero-Shot Strategies for Length-Controllable Summarization](https://mlsanity.com/paper/2501.00233/) (2024)
- [Length Controlled Generation for Black-box LLMs](https://mlsanity.com/paper/2412.14656/) (2024)
- [Can LLMs Track Their Output Length? A Dynamic Feedback Mechanism for Precise Length Regulation](https://mlsanity.com/paper/2601.01768/) (2026)
- [Summarization with Precise Length Control](https://mlsanity.com/paper/2305.05171/) (2023)
- [Prompt-Based One-Shot Exact Length-Controlled Generation with LLMs](https://mlsanity.com/paper/2508.13805/) (2025)
- [Fine-grained Video Dubbing Duration Alignment with Segment Supervised Preference Optimization](https://mlsanity.com/paper/2508.08550/) (2025)
- [Fine-grained length controllable video captioning with ordinal embeddings](https://mlsanity.com/paper/2408.15447/) (2024)
- [Hansel: Output Length Controlling Framework for Large Language Models](https://mlsanity.com/paper/2412.14033/) (2024)
- [Unsupervised Extractive Summarization with Learnable Length Control Strategies](https://mlsanity.com/paper/2312.06901/) (2023)
- [Controlling Summarization Length Through EOS Token Weighting](https://mlsanity.com/paper/2506.05017/) (2025)
- [Progress Ratio Embeddings: An Impatience Signal for Robust Length Control in Neural Text Generation](https://mlsanity.com/paper/2512.06938/) (2025)

## Abstract (arXiv)

Fixed length summarization aims at generating summaries with a preset number of words or characters. Most recent researches incorporate length information with word embeddings as the input to the recurrent decoding unit, causing a compromise between length controllability and summary quality. In this work, we present an effective length controlling unit Length Attention (LenAtten) to break this trade-off. Experimental results show that LenAtten not only brings improvements in length controllability and ROGUE scores but also has great generalization ability. In the task of generating a summary with the target length, our model is 732 times better than the best-performing length controllable summarizer in length controllability on the CNN/Daily Mail dataset.