# Learning to Encode Text as Human-Readable Summaries using Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1810.02851 · HTML: https://mlsanity.com/paper/1810.02851/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2018.

## Built on by (most cited first, 11 of 11)

- [CiteSum: Citation Text-guided Scientific Extreme Summarization and Domain Adaptation with Limited Supervision](https://mlsanity.com/paper/2205.06207/) (2022)
- [Text Compression-aided Transformer Encoding](https://mlsanity.com/paper/2102.05951/) (2021)
- [A Character-Level Length-Control Algorithm for Non-Autoregressive Sentence Summarization](https://mlsanity.com/paper/2205.14522/) (2022)
- [Learning Non-Autoregressive Models from Search for Unsupervised Sentence Summarization](https://mlsanity.com/paper/2205.14521/) (2022)
- [Unsupervised Multi-Granularity Summarization](https://mlsanity.com/paper/2201.12502/) (2022)
- [Generating Multiple-Length Summaries via Reinforcement Learning for Unsupervised Sentence Summarization](https://mlsanity.com/paper/2212.10843/) (2022)
- [A New Sentence Extraction Strategy for Unsupervised Extractive Summarization Methods](https://mlsanity.com/paper/2112.03203/) (2021)
- [RTSUM: Relation Triple-based Interpretable Summarization with Multi-level Salience Visualization](https://mlsanity.com/paper/2310.13895/) (2023)
- [Unsupervised Extractive Summarization with Learnable Length Control Strategies](https://mlsanity.com/paper/2312.06901/) (2023)
- [Paired by the Teacher: Turning Unpaired Data into High-Fidelity Pairs for Low-Resource Text Generation](https://mlsanity.com/paper/2509.25144/) (2025)
- [Evaluation of Abstractive Summarisation Models with Machine Translation in Deliberative Processes](https://mlsanity.com/paper/2110.05847/) (2021)

## Abstract (arXiv)

Auto-encoders compress input data into a latent-space representation and reconstruct the original data from the representation. This latent representation is not easily interpreted by humans. In this paper, we propose training an auto-encoder that encodes input text into human-readable sentences, and unpaired abstractive summarization is thereby achieved. The auto-encoder is composed of a generator and a reconstructor. The generator encodes the input text into a shorter word sequence, and the reconstructor recovers the generator input from the generator output. To make the generator output human-readable, a discriminator restricts the output of the generator to resemble human-written sentences. By taking the generator output as the summary of the input text, abstractive summarization is achieved without document-summary pairs as training data. Promising results are shown on both English and Chinese corpora.