mlsanityData to 7 Oct 2026

Deep Recurrent Generative Decoder for Abstractive Text Summarization

arXiv 1708.00625 ↗First posted 2 Aug 2017cs.CL cs.AIcited by 19

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Abstract (arXiv)

We propose a new framework for abstractive text summarization based on a sequence-to-sequence oriented encoder-decoder model equipped with a deep recurrent generative decoder (DRGN). Latent structure information implied in the target summaries is learned based on a recurrent latent random model for improving the summarization quality. Neural variational inference is employed to address the intractable posterior inference for the recurrent latent variables. Abstractive summaries are generated based on both the generative latent variables and the discriminative deterministic states. Extensive experiments on some benchmark datasets in different languages show that DRGN achieves improvements over the state-of-the-art methods.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Aug 2017.

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Citing papers per year: 2021: 8; 2022: 5; 2023: 3; 2024: 2; 2025: 1.

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  1. 2024A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methodscited by 101
  2. 2023Large Language Models are Not Yet Human-Level Evaluators for Abstractive Summarizationcited by 70
  3. 2022Latent Diffusion Energy-Based Model for Interpretable Text Modelingcited by 67
  4. 2021Non-Autoregressive Text Generation with Pre-trained Language Modelscited by 36
  5. 2022Data-to-text Generation with Variational Sequential Planningcited by 17
  6. 2022Automatic Text Summarization Methods: A Comprehensive Reviewcited by 17
  7. 2021EEGFuseNet: Hybrid Unsupervised Deep Feature Characterization and Fusion for High-Dimensional EEG with An Application to Emotion Recognitioncited by 15
  8. 2021Latent Space Energy-Based Model of Symbol-Vector Coupling for Text Generation and Classificationcited by 14
  9. 2021A New Approach to Overgenerating and Scoring Abstractive Summariescited by 13
  10. 2021ImaginE: An Imagination-Based Automatic Evaluation Metric for Natural Language Generationcited by 6
  11. 2022N-gram Is Back: Residual Learning of Neural Text Generation with n-gram Language Modelcited by 3
  12. 2023Abstractive Text Summarization Using the BRIO Training Paradigmcited by 1
7 more
  1. 2023Variational Connectionist Temporal Classification for Order-Preserving Sequence Modelingcited by 1
  2. 2024Deep Transfer Learning Based Peer Review Aggregation and Meta-review Generation for Scientific Articlescited by 1
  3. 2025CSTRL: Context-Driven Sequential Transfer Learning for Abstractive Radiology Report Summarizationcited by 1
  4. 2021Alleviate Exposure Bias in Sequence Prediction with Recurrent Neural Networkscited by 0
  5. 2021Reinforcing Semantic-Symmetry for Document Summarizationcited by 0
  6. 2021Topic-Aware Encoding for Extractive Summarizationcited by 0
  7. 2022Brand Celebrity Matching Model Based on Natural Language Processingcited by 0