DeepAI
Log In Sign Up

Unsupervised Extractive Summarization by Human Memory Simulation

04/16/2021
by   Ronald Cardenas, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

05/20/2022

On the Trade-off between Redundancy and Local Coherence in Summarization

Extractive summarization systems are known to produce poorly coherent an...
01/26/2021

Unsupervised Abstractive Summarization of Bengali Text Documents

Abstractive summarization systems generally rely on large collections of...
02/26/2018

Tone Biased MMR Text Summarization

Text summarization is an interesting area for researchers to develop new...
04/13/2020

A Divide-and-Conquer Approach to the Summarization of Academic Articles

We present a novel divide-and-conquer method for the summarization of lo...
04/06/2020

At Which Level Should We Extract? An Empirical Study on Extractive Document Summarization

Extractive methods have proven to be very effective in automatic documen...
10/04/2021

Leveraging Information Bottleneck for Scientific Document Summarization

This paper presents an unsupervised extractive approach to summarize sci...
11/09/2020

Automatic Summarization of Open-Domain Podcast Episodes

We present implementation details of our abstractive summarizers that ac...