# Deep Recurrent Generative Decoder for Abstractive Text Summarization

arXiv: https://arxiv.org/abs/1708.00625 · HTML: https://mlsanity.com/paper/1708.00625/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2017.

## Built on by (most cited first, 19 of 19)

- [A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methods](https://mlsanity.com/paper/2403.02901/) (2024)
- [Large Language Models are Not Yet Human-Level Evaluators for Abstractive Summarization](https://mlsanity.com/paper/2305.13091/) (2023)
- [Latent Diffusion Energy-Based Model for Interpretable Text Modeling](https://mlsanity.com/paper/2206.05895/) (2022)
- [Non-Autoregressive Text Generation with Pre-trained Language Models](https://mlsanity.com/paper/2102.08220/) (2021)
- [Data-to-text Generation with Variational Sequential Planning](https://mlsanity.com/paper/2202.13756/) (2022)
- [Automatic Text Summarization Methods: A Comprehensive Review](https://mlsanity.com/paper/2204.01849/) (2022)
- [EEGFuseNet: Hybrid Unsupervised Deep Feature Characterization and Fusion for High-Dimensional EEG with An Application to Emotion Recognition](https://mlsanity.com/paper/2102.03777/) (2021)
- [Latent Space Energy-Based Model of Symbol-Vector Coupling for Text Generation and Classification](https://mlsanity.com/paper/2108.11556/) (2021)
- [A New Approach to Overgenerating and Scoring Abstractive Summaries](https://mlsanity.com/paper/2104.01726/) (2021)
- [ImaginE: An Imagination-Based Automatic Evaluation Metric for Natural Language Generation](https://mlsanity.com/paper/2106.05970/) (2021)
- [N-gram Is Back: Residual Learning of Neural Text Generation with n-gram Language Model](https://mlsanity.com/paper/2210.14431/) (2022)
- [Abstractive Text Summarization Using the BRIO Training Paradigm](https://mlsanity.com/paper/2305.13696/) (2023)
- [Variational Connectionist Temporal Classification for Order-Preserving Sequence Modeling](https://mlsanity.com/paper/2309.11983/) (2023)
- [Deep Transfer Learning Based Peer Review Aggregation and Meta-review Generation for Scientific Articles](https://mlsanity.com/paper/2410.04202/) (2024)
- [CSTRL: Context-Driven Sequential Transfer Learning for Abstractive Radiology Report Summarization](https://mlsanity.com/paper/2503.05750/) (2025)
- [Alleviate Exposure Bias in Sequence Prediction  with Recurrent Neural Networks](https://mlsanity.com/paper/2103.11603/) (2021)
- [Reinforcing Semantic-Symmetry for Document Summarization](https://mlsanity.com/paper/2112.07583/) (2021)
- [Topic-Aware Encoding for Extractive Summarization](https://mlsanity.com/paper/2112.09572/) (2021)
- [Brand Celebrity Matching Model Based on Natural Language Processing](https://mlsanity.com/paper/2208.08887/) (2022)

## 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.