Multi-Document Extractive Summarization as a Non-linear Combinatorial Optimization Problem

Meghana N. Satpute (), Luobing Dong (), Weili Wu () and Ding-Zhu Du ()
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Meghana N. Satpute: The University of Texas at Dallas
Luobing Dong: School of Telecommunications Engineering, Xidian University
Weili Wu: The University of Texas at Dallas
Ding-Zhu Du: The University of Texas at Dallas

A chapter in Nonlinear Combinatorial Optimization, 2019, pp 295-308 from Springer

Abstract: Abstract Multi-document summarization deals with finding the core theme presented in multiple documents. This can be done by selecting the important information from the text in the multiple documents. Extractive summarization selects and extracts such sentences which represent the gist of the documents. In this paper, we have surveyed how research in multi-document summarization has evolved from simple sentence-based techniques like sentence position to complex neural network based supervised learning techniques. In recent years, more and more supervised learning methods are proposed to tackle this problem along with some unsupervised approaches described in LSA (Deerwester et al. J Am Soc Inf Sci 41(6): 391–407, 1990) and TextRank (Mihalcea et al. Textrank: Bringing order into text. In: Proceedings of the 2004 conference on empirical methods in natural language processing, 2004). In this chapter, we have proposed an alternative unsupervised method where the problem of multi-document summarization can be viewed as a non-linear combinatorial optimization problem. We have formulated the problem and discussed possible solution to this problem.

Date: 2019
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DOI: 10.1007/978-3-030-16194-1_15

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