Extractive Multi Document Summarization using Dynamical Measurements of Complex Networks

TL;DR

This paper introduces a novel extractive multi-document summarization method that leverages complex network dynamics, such as symmetry and accessibility, to identify the most central and relevant sentences across multiple texts.

Contribution

The study proposes a new approach using dynamical measurements of complex networks to improve extractive multi-document summarization performance.

Findings

Excellent results with dynamical network measurements like random walks.

Network characterization improves relevance of extracted sentences.

Method outperforms some existing summarization techniques.

Abstract

Due to the large amount of textual information available on Internet, it is of paramount relevance to use techniques that find relevant and concise content. A typical task devoted to the identification of informative sentences in documents is the so called extractive document summarization task. In this paper, we use complex network concepts to devise an extractive Multi Document Summarization (MDS) method, which extracts the most central sentences from several textual sources. In the proposed model, texts are represented as networks, where nodes represent sentences and the edges are established based on the number of shared words. Differently from previous works, the identification of relevant terms is guided by the characterization of nodes via dynamical measurements of complex networks, including symmetry, accessibility and absorption time. The evaluation of the proposed system revealed that excellent results were obtained with particular dynamical measurements, including those based on the exploration of networks via random walks.