  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. These results suggest that patterns in the discourse
structure are a strong signal for determining importance in scientific
articles.
