Unsupervised Extractive Summarization by Human Memory Simulation

Understand

Summarization systems face the core challenge of identifying and selecting important information.

• In this paper, we tackle the problem of content selection in unsupervised extractive summarization of long, structured documents.
• We introduce a wide range of heuristics that leverage cognitive representations of content units and how these are retained or forgotten in human memory.
• We find that properties of these representations of human memory can be exploited to capture relevance of content units in scientific articles.