Combining a Multi-Document Update Summarization System –CBSEAS– with a Genetic Algorithm

Abstract

In this paper, we present a combination of a multi-document summarization system with a genetic algorithm. We first introduce a novel approach for automatic summarization. CBSEAS, the system which implements this approach, integrates a new method to detect redundancy at its very core in order to produce summaries with a good informational diversity. However, the evaluation of our system at TAC 2008 -Text Analysis Conference-revealed that system adaptation to a specific domain is fundamental to obtain summaries of an acceptable quality. The second part of this paper is dedicated to a genetic algorithm which aims to adapt our system to specific domains. We present its evaluation by TAC 2009 on a newswire articles summarization task and show that this optimization is having a great influence on both human and automatic evaluations.

Automatic Multi Document Summarization Approaches

“…The use of PSO for optimization have also been proven to be robust in other domains as well (Nacy et al, 2009, Balaji andKamaraj, 2011). Another weight learning approach was described by Bossard and Rodrigues (2011) who approximated the best weight combination using a genetic algorithm for their multi document summarizer. By using the genetic algorithm, a suitable combination of feature weights can be found.…”

“…Weight learning method (Osborne, 2002) Conjugate gradient decent search method (Fattah and Ren, 2009) Mathematical Regression (MR) model (Binwahlan et al, 2009) Particle Swarm Optimization (PSO) (Dehkordi et al, 2009), Genetic Algorithm (GA) model (Bossard and Rodrigues, 2011) and (Suanmali et al, 2011) Besides optimizing feature weights, the impact of combining different features has been investigated by Hariharan (2010) for multi document summarization. In his study, the author showed that term frequency weight combined with position and node weight feature yields significantly better results.…”

A Modular Tool for Automatic Summarization

“…Optimizing these parameters for a specific task is crucial. Litvak et al (2010); Bossard and Rodrigues (2011) have shown that a genetic algorithm (GA) can be efficient for this kind of optimization. Our tool integrates a GA to optimize summarization methods hyperparameters.…”

An Evolutionary Algorithm for Automatic Summarization

“…Supervised Learning (Litvak et al, 2010;Bossard and Rodrigues, 2011) use genetic algorithms for supervised learning of parameters in order to tune automatic summarization systems. Nishikawa et al (2014); Takamura and Okumura (2010); Sipos et al (2012) perform structured output learning to maximize ROUGE scores.…”