# Sequential Copying Networks

arXiv: https://arxiv.org/abs/1807.02301 · HTML: https://mlsanity.com/paper/1807.02301/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2018.

## Built on by (most cited first, 10 of 10)

- [CoditT5: Pretraining for Source Code and Natural Language Editing](https://mlsanity.com/paper/2208.05446/) (2022)
- [Sequence-to-Sequence Learning with Latent Neural Grammars](https://mlsanity.com/paper/2109.01135/) (2021)
- [Assemble Foundation Models for Automatic Code Summarization](https://mlsanity.com/paper/2201.05222/) (2022)
- [Entity-based SpanCopy for Abstractive Summarization to Improve the Factual Consistency](https://mlsanity.com/paper/2209.03479/) (2022)
- [May the Force Be with Your Copy Mechanism: Enhanced Supervised-Copy Method for Natural Language Generation](https://mlsanity.com/paper/2112.10360/) (2021)
- [PROM: A Phrase-level Copying Mechanism with Pre-training for Abstractive Summarization](https://mlsanity.com/paper/2305.06647/) (2023)
- [Neural Abstractive Unsupervised Summarization of Online News Discussions](https://mlsanity.com/paper/2106.03953/) (2021)
- [Semantic-Based Self-Critical Training For Question Generation](https://mlsanity.com/paper/2108.12026/) (2021)
- [Exploring Answer Information Methods for Question Generation with Transformers](https://mlsanity.com/paper/2312.03483/) (2023)
- [HintMiner: Automatic Question Hints Mining From Q&A Web Posts with Language Model via Self-Supervised Learning](https://mlsanity.com/paper/2609.16060/) (2026)

## Abstract (arXiv)

Copying mechanism shows effectiveness in sequence-to-sequence based neural network models for text generation tasks, such as abstractive sentence summarization and question generation. However, existing works on modeling copying or pointing mechanism only considers single word copying from the source sentences. In this paper, we propose a novel copying framework, named Sequential Copying Networks (SeqCopyNet), which not only learns to copy single words, but also copies sequences from the input sentence. It leverages the pointer networks to explicitly select a sub-span from the source side to target side, and integrates this sequential copying mechanism to the generation process in the encoder-decoder paradigm. Experiments on abstractive sentence summarization and question generation tasks show that the proposed SeqCopyNet can copy meaningful spans and outperforms the baseline models.