  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.
Experiments show that our proposed heuristics are effective at leveraging
cognitive structures and the organization of the document (i.e.\ sections of an
article), and automatic and human evaluations provide strong evidence that
these heuristics extract more summary-worthy content units.
