# Global Encoding for Abstractive Summarization

arXiv: https://arxiv.org/abs/1805.03989 · HTML: https://mlsanity.com/paper/1805.03989/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2018.

## Built on by (most cited first, 16 of 16)

- [A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methods](https://mlsanity.com/paper/2403.02901/) (2024)
- [An Iterative Optimizing Framework for Radiology Report Summarization with ChatGPT](https://mlsanity.com/paper/2304.08448/) (2023)
- [ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation](https://mlsanity.com/paper/2104.08006/) (2021)
- [Towards Improving Faithfulness in Abstractive Summarization](https://mlsanity.com/paper/2210.01877/) (2022)
- [Word Graph Guided Summarization for Radiology Findings](https://mlsanity.com/paper/2112.09925/) (2021)
- [Explicit Syntactic Guidance for Neural Text Generation](https://mlsanity.com/paper/2306.11485/) (2023)
- [An Analysis of Abstractive Text Summarization Using Pre-trained Models](https://mlsanity.com/paper/2303.12796/) (2023)
- [WikiDes: A Wikipedia-Based Dataset for Generating Short Descriptions from Paragraphs](https://mlsanity.com/paper/2209.13101/) (2022)
- [Transformers Go for the LOLs: Generating (Humourous) Titles from Scientific Abstracts End-to-End](https://mlsanity.com/paper/2212.10522/) (2022)
- [Follow the Timeline! Generating Abstractive and Extractive Timeline Summary in Chronological Order](https://mlsanity.com/paper/2301.00867/) (2023)
- [IOT: Instance-wise Layer Reordering for Transformer Structures](https://mlsanity.com/paper/2103.03457/) (2021)
- [Abstractive Text Summarization using Attentive GRU based Encoder-Decoder](https://mlsanity.com/paper/2302.13117/) (2023)
- [Neural Abstractive Unsupervised Summarization of Online News Discussions](https://mlsanity.com/paper/2106.03953/) (2021)
- [Towards Controlled and Diverse Generation of Article Comments](https://mlsanity.com/paper/2107.11781/) (2021)
- [StyleBART: Decorate Pretrained Model with Style Adapters for Unsupervised Stylistic Headline Generation](https://mlsanity.com/paper/2310.17743/) (2023)
- [Hierarchical Multi-Label Generation with Probabilistic Level-Constraint](https://mlsanity.com/paper/2505.03775/) (2025)

## Abstract (arXiv)

In neural abstractive summarization, the conventional sequence-to-sequence (seq2seq) model often suffers from repetition and semantic irrelevance. To tackle the problem, we propose a global encoding framework, which controls the information flow from the encoder to the decoder based on the global information of the source context. It consists of a convolutional gated unit to perform global encoding to improve the representations of the source-side information. Evaluations on the LCSTS and the English Gigaword both demonstrate that our model outperforms the baseline models, and the analysis shows that our model is capable of reducing repetition.