# Generative Adversarial Network for Abstractive Text Summarization

arXiv: https://arxiv.org/abs/1711.09357 · HTML: https://mlsanity.com/paper/1711.09357/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2017.

## Built on by (most cited first, 23 of 23)

- [DOC2PPT: Automatic Presentation Slides Generation from Scientific Documents](https://mlsanity.com/paper/2101.11796/) (2021)
- [Towards Improving Faithfulness in Abstractive Summarization](https://mlsanity.com/paper/2210.01877/) (2022)
- [Unifying GANs and Score-Based Diffusion as Generative Particle Models](https://mlsanity.com/paper/2305.16150/) (2023)
- [Low-Resource Dialogue Summarization with Domain-Agnostic Multi-Source Pretraining](https://mlsanity.com/paper/2109.04080/) (2021)
- [A Review of Text Style Transfer using Deep Learning](https://mlsanity.com/paper/2109.15144/) (2021)
- [SongDriver: Real-time Music Accompaniment Generation without Logical Latency nor Exposure Bias](https://mlsanity.com/paper/2209.06054/) (2022)
- [Rethinking Self-Supervision Objectives for Generalizable Coherence Modeling](https://mlsanity.com/paper/2110.07198/) (2021)
- [Enhanced word embeddings using multi-semantic representation through lexical chains](https://mlsanity.com/paper/2101.09023/) (2021)
- [Biomedical text summarization using Conditional Generative Adversarial Network(CGAN)](https://mlsanity.com/paper/2110.11870/) (2021)
- [Extractive Summarization of Call Transcripts](https://mlsanity.com/paper/2103.10599/) (2021)
- [Semantic Extractor-Paraphraser based Abstractive Summarization](https://mlsanity.com/paper/2105.01296/) (2021)
- [Evaluating Privacy Questions From Stack Overflow: Can ChatGPT Compete?](https://mlsanity.com/paper/2306.11174/) (2023)
- [Surveying the Landscape of Text Summarization with Deep Learning: A Comprehensive Review](https://mlsanity.com/paper/2310.09411/) (2023)
- [Benchmarking LLMs on the Semantic Overlap Summarization Task](https://mlsanity.com/paper/2402.17008/) (2024)
- [SensPick: Sense Picking for Word Sense Disambiguation](https://mlsanity.com/paper/2102.05260/) (2021)
- [News Meets Microblog: Hashtag Annotation via Retriever-Generator](https://mlsanity.com/paper/2104.08723/) (2021)
- [Text Summarization of Czech News Articles Using Named Entities](https://mlsanity.com/paper/2104.10454/) (2021)
- [Deep Transfer Learning Based Peer Review Aggregation and Meta-review Generation for Scientific Articles](https://mlsanity.com/paper/2410.04202/) (2024)
- [Multi-Narrative Semantic Overlap Task: Evaluation and Benchmark](https://mlsanity.com/paper/2201.05294/) (2022)
- [A Template-based Method for Constrained Neural Machine Translation](https://mlsanity.com/paper/2205.11255/) (2022)
- [Inducing Causal Structure for Abstractive Text Summarization](https://mlsanity.com/paper/2308.12888/) (2023)
- [A Hybrid Strategy for Chat Transcript Summarization](https://mlsanity.com/paper/2402.01510/) (2024)
- [Conversational Text Extraction with Large Language Models Using Retrieval-Augmented Systems](https://mlsanity.com/paper/2501.09801/) (2025)

## Abstract (arXiv)

In this paper, we propose an adversarial process for abstractive text summarization, in which we simultaneously train a generative model G and a discriminative model D. In particular, we build the generator G as an agent of reinforcement learning, which takes the raw text as input and predicts the abstractive summarization. We also build a discriminator which attempts to distinguish the generated summary from the ground truth summary. Extensive experiments demonstrate that our model achieves competitive ROUGE scores with the state-of-the-art methods on CNN/Daily Mail dataset. Qualitatively, we show that our model is able to generate more abstractive, readable and diverse summaries.