# Unsupervised Extractive Summarization by Human Memory Simulation

arXiv: https://arxiv.org/abs/2104.08392 · HTML: https://mlsanity.com/paper/2104.08392/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 16 Apr 2021.

## Builds on (most cited first, 9 of 9)

- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents](https://mlsanity.com/paper/1804.05685/) (2018)
- [Ranking Sentences for Extractive Summarization with Reinforcement Learning](https://mlsanity.com/paper/1802.08636/) (2018)
- [Sentence Centrality Revisited for Unsupervised Summarization](https://mlsanity.com/paper/1906.03508/) (2019)
- [Content Selection in Deep Learning Models of Summarization](https://mlsanity.com/paper/1810.12343/) (2018)
- [Pretrained Language Models for Sequential Sentence Classification](https://mlsanity.com/paper/1909.04054/) (2019)
- [Discrete Optimization for Unsupervised Sentence Summarization with Word-Level Extraction](https://mlsanity.com/paper/2005.01791/) (2020)
- [What is this Article about? Extreme Summarization with Topic-aware Convolutional Neural Networks](https://mlsanity.com/paper/1907.08722/) (2019)

## Built on by (most cited first, 1 of 1)

- [Summarization with Graphical Elements](https://mlsanity.com/paper/2204.07551/) (2022)

## Abstract (arXiv)

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.