# Unsupervised Abstractive Sentence Summarization using Length Controlled Variational Autoencoder

arXiv: https://arxiv.org/abs/1809.05233 · HTML: https://mlsanity.com/paper/1809.05233/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2018.

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

- [Abstractive Meeting Summarization: A Survey](https://mlsanity.com/paper/2208.04163/) (2022)
- [Multi-Narrative Semantic Overlap Task: Evaluation and Benchmark](https://mlsanity.com/paper/2201.05294/) (2022)
- [GCVAE: Generalized-Controllable Variational AutoEncoder](https://mlsanity.com/paper/2206.04225/) (2022)
- [Boosting Summarization with Normalizing Flows and Aggressive Training](https://mlsanity.com/paper/2311.00588/) (2023)

## Abstract (arXiv)

In this work we present an unsupervised approach to summarize sentences in abstractive way using Variational Autoencoder (VAE). VAE are known to learn a semantically rich latent variable, representing high dimensional input. VAEs are trained by learning to reconstruct the input from the probabilistic latent variable. Explicitly providing the information about output length during training influences the VAE to not encode this information and thus can be manipulated during inference. Instructing the decoder to produce a shorter output sequence leads to expressing the input sentence with fewer words. We show on different summarization data sets, that these shorter sentences can not beat a simple baseline but yield higher ROUGE scores than trying to reconstruct the whole sentence.