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Generative Adversarial Network for Abstractive Text Summarization

arXiv 1711.09357 ↗First posted 26 Nov 2017cs.CL cs.AIcited by 23

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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.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Nov 2017.

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Citing papers per year: 2021: 11; 2022: 4; 2023: 4; 2024: 3; 2025: 1.

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  1. 2021DOC2PPT: Automatic Presentation Slides Generation from Scientific Documentscited by 68
  2. 2022Towards Improving Faithfulness in Abstractive Summarizationcited by 27
  3. 2023Unifying GANs and Score-Based Diffusion as Generative Particle Modelscited by 21
  4. 2021Low-Resource Dialogue Summarization with Domain-Agnostic Multi-Source Pretrainingcited by 19
  5. 2021A Review of Text Style Transfer using Deep Learningcited by 18
  6. 2022SongDriver: Real-time Music Accompaniment Generation without Logical Latency nor Exposure Biascited by 13
  7. 2021Rethinking Self-Supervision Objectives for Generalizable Coherence Modelingcited by 8
  8. 2021Enhanced word embeddings using multi-semantic representation through lexical chainscited by 7
  9. 2021Biomedical text summarization using Conditional Generative Adversarial Network(CGAN)cited by 3
  10. 2021Extractive Summarization of Call Transcriptscited by 2
  11. 2021Semantic Extractor-Paraphraser based Abstractive Summarizationcited by 2
  12. 2023Evaluating Privacy Questions From Stack Overflow: Can ChatGPT Compete?cited by 2
11 more
  1. 2023Surveying the Landscape of Text Summarization with Deep Learning: A Comprehensive Reviewcited by 2
  2. 2024Benchmarking LLMs on the Semantic Overlap Summarization Taskcited by 2
  3. 2021SensPick: Sense Picking for Word Sense Disambiguationcited by 1
  4. 2021News Meets Microblog: Hashtag Annotation via Retriever-Generatorcited by 1
  5. 2021Text Summarization of Czech News Articles Using Named Entitiescited by 1
  6. 2024Deep Transfer Learning Based Peer Review Aggregation and Meta-review Generation for Scientific Articlescited by 1
  7. 2022Multi-Narrative Semantic Overlap Task: Evaluation and Benchmarkcited by 0
  8. 2022A Template-based Method for Constrained Neural Machine Translationcited by 0
  9. 2023Inducing Causal Structure for Abstractive Text Summarizationcited by 0
  10. 2024A Hybrid Strategy for Chat Transcript Summarizationcited by 0
  11. 2025Conversational Text Extraction with Large Language Models Using Retrieval-Augmented Systemscited by 0