# Structure-Infused Copy Mechanisms for Abstractive Summarization

arXiv: https://arxiv.org/abs/1806.05658 · HTML: https://mlsanity.com/paper/1806.05658/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2018.

## Built on by (most cited first, 17 of 17)

- [A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Models](https://mlsanity.com/paper/2406.11289/) (2024)
- [Conditional Generation with a Question-Answering Blueprint](https://mlsanity.com/paper/2207.00397/) (2022)
- [Prompted Opinion Summarization with GPT-3.5](https://mlsanity.com/paper/2211.15914/) (2022)
- [mFACE: Multilingual Summarization with Factual Consistency Evaluation](https://mlsanity.com/paper/2212.10622/) (2022)
- [See, Hear, Read: Leveraging Multimodality with Guided Attention for Abstractive Text Summarization](https://mlsanity.com/paper/2105.09601/) (2021)
- [Controllable Summarization with Constrained Markov Decision Process](https://mlsanity.com/paper/2108.03405/) (2021)
- [Abstractive Text Summarization: State of the Art, Challenges, and Improvements](https://mlsanity.com/paper/2409.02413/) (2024)
- [Proposition-Level Clustering for Multi-Document Summarization](https://mlsanity.com/paper/2112.08770/) (2021)
- [A Modular Approach for Multimodal Summarization of TV Shows](https://mlsanity.com/paper/2403.03823/) (2024)
- [Enriching Transformers with Structured Tensor-Product Representations for Abstractive Summarization](https://mlsanity.com/paper/2106.01317/) (2021)
- [Text-Blueprint: An Interactive Platform for Plan-based Conditional Generation](https://mlsanity.com/paper/2305.00034/) (2023)
- [SgSum: Transforming Multi-document Summarization into Sub-graph Selection](https://mlsanity.com/paper/2110.12645/) (2021)
- [Fusing Multimodal Signals on Hyper-complex Space for Extreme Abstractive Text Summarization (TL;DR) of Scientific Contents](https://mlsanity.com/paper/2306.13968/) (2023)
- [Abstractive Summarization Guided by Latent Hierarchical Document Structure](https://mlsanity.com/paper/2211.09458/) (2022)
- [Evaluation of Abstractive Summarisation Models with Machine Translation in Deliberative Processes](https://mlsanity.com/paper/2110.05847/) (2021)
- [TrICy: Trigger-guided Data-to-text Generation with Intent aware Attention-Copy](https://mlsanity.com/paper/2402.01714/) (2024)
- [InforME: Improving Informativeness of Abstractive Text Summarization With Informative Attention Guided by Named Entity Salience](https://mlsanity.com/paper/2510.05769/) (2025)

## Abstract (arXiv)

Seq2seq learning has produced promising results on summarization. However, in many cases, system summaries still struggle to keep the meaning of the original intact. They may miss out important words or relations that play critical roles in the syntactic structure of source sentences. In this paper, we present structure-infused copy mechanisms to facilitate copying important words and relations from the source sentence to summary sentence. The approach naturally combines source dependency structure with the copy mechanism of an abstractive sentence summarizer. Experimental results demonstrate the effectiveness of incorporating source-side syntactic information in the system, and our proposed approach compares favorably to state-of-the-art methods.