Concept Pointer Network for Abstractive Summarization

By Wang Wenbo, Yang Gao, Huang Heyan, Yuxiang Zhou. 2019

40 citations recorded by the source corpus.

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 distant-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.

Subfields: Artificial Intelligence

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yu Bai
2. Jie Hao
3. Yang Gao
4. Marti A. Hearst
5. John F. Canny
6. Andrew Hsi Bloomberg
7. Chenlei Guo
8. Xing Fan

Citations in the corpus

• Overcoming Catastrophic Forgetting During Domain Adaptation of Seq2seq Language Generation (2022, background): “Some past work in LLL has demonstrated that discriminative models can be incrementally learnt for a sequence of tasks (Kirkpatrick et al., 2017; Chen et al., 2020; Wang et al., 2019).”
• PSP: Pre-trained Soft Prompts for Few-Shot Abstractive Summarization (2022, background): “This outcome is detrimental to summarization that requires to understand the semantics and inner discourse structure of documents (Wang et al., 2019).”
• The Summary Loop: Learning to Write Abstractive Summaries Without Examples (2020, methodology): “Performance can further be improved by constraining the attention (Gehrmann et al., 2018; Gui et al., 2019; Wang et al., 2019) and using pretrained Transformer-based language models (Lewis et al., 2019; Chi et al., 2019; Edunov et al., 2019).”