# Concept Pointer Network for Abstractive Summarization

arXiv: https://arxiv.org/abs/1910.08486 · HTML: https://mlsanity.com/paper/1910.08486/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2019.

## Built on by (most cited first, 13 of 13)

- [Non-Autoregressive Text Generation with Pre-trained Language Models](https://mlsanity.com/paper/2102.08220/) (2021)
- [The Summary Loop: Learning to Write Abstractive Summaries Without Examples](https://mlsanity.com/paper/2105.05361/) (2021)
- [PSP: Pre-trained Soft Prompts for Few-Shot Abstractive Summarization](https://mlsanity.com/paper/2204.04413/) (2022)
- [Mask the Correct Tokens: An Embarrassingly Simple Approach for Error Correction](https://mlsanity.com/paper/2211.13252/) (2022)
- [Nutri-bullets: Summarizing Health Studies by Composing Segments](https://mlsanity.com/paper/2103.11921/) (2021)
- [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)
- [Nutribullets Hybrid: Multi-document Health Summarization](https://mlsanity.com/paper/2104.03465/) (2021)
- [On the Role of Entity and Event Level Conceptualization in Generalizable Reasoning: A Survey of Tasks, Methods, Applications, and Future Directions](https://mlsanity.com/paper/2406.10885/) (2024)
- [Weakly Supervised Concept Map Generation through Task-Guided Graph Translation](https://mlsanity.com/paper/2110.15720/) (2021)
- [Unsupervised Keyphrase Extraction via Interpretable Neural Networks](https://mlsanity.com/paper/2203.07640/) (2022)
- [Sentiment analysis and opinion mining on E-commerce site](https://mlsanity.com/paper/2211.15536/) (2022)
- [M3-Impute: Mask-guided Representation Learning for Missing Value Imputation](https://mlsanity.com/paper/2410.08794/) (2024)

## Abstract (arXiv)

A quality abstractive summary should not only copy salient source texts as summaries but should also tend to generate new conceptual words to express concrete details. Inspired by the popular pointer generator sequence-to-sequence model, this paper presents a concept pointer network for improving these aspects of abstractive summarization. The network leverages knowledge-based, context-aware conceptualizations to derive an extended set of candidate concepts. The model then points to the most appropriate choice using both the concept set and original source text. This joint approach generates abstractive summaries with higher-level semantic concepts. The training model is also optimized in a way that adapts to different data, which is based on a novel method of distantly-supervised learning guided by reference summaries and testing set. Overall, the proposed approach provides statistically significant improvements over several state-of-the-art models on both the DUC-2004 and Gigaword datasets. A human evaluation of the model's abstractive abilities also supports the quality of the summaries produced within this framework.