Enhancing Extractive Text Summarization with Topic-Aware Graph Neural Networks

Understand

Text summarization aims to compress a textual document to a short summary while keeping salient information.

• Extractive approaches are widely used in text summarization because of their fluency and efficiency.
• However, most of existing extractive models hardly capture inter-sentence relationships, particularly in long documents.
• They also often ignore the effect of topical information on capturing important contents.