Concept Pointer Network for Abstractive Summarization

Understand

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.