Discourse-Aware Unsupervised Summarization of Long Scientific Documents

Understand

We propose an unsupervised graph-based ranking model for extractive summarization of long scientific documents.

• Our method assumes a two-level hierarchical graph representation of the source document, and exploits asymmetrical positional cues to determine sentence importance.
• Results on the PubMed and arXiv datasets show that our approach outperforms strong unsupervised baselines by wide margins in automatic metrics and human evaluation.
• In addition, it achieves performance comparable to many state-of-the-art supervised approaches which are trained on hundreds of thousands of examples.