Métodos de Otimização Combinatória Aplicados ao Problema de Compressão MultiFrases
This addresses the challenge of efficiently processing large volumes of textual data for NLP applications, though it appears incremental as it builds on existing optimization methods for MSC.
The paper tackled the problem of Multi-Sentences Compression (MSC) to reduce sentence length without losing core information, using Combinatorial Optimization and Graph Theory, and reported achieving very good quality and outperforming state-of-the-art methods in experiments on 40 sentence clusters.
The Internet has led to a dramatic increase in the amount of available information. In this context, reading and understanding this flow of information have become costly tasks. In the last years, to assist people to understand textual data, various Natural Language Processing (NLP) applications based on Combinatorial Optimization have been devised. However, for Multi-Sentences Compression (MSC), method which reduces the sentence length without removing core information, the insertion of optimization methods requires further study to improve the performance of MSC. This article describes a method for MSC using Combinatorial Optimization and Graph Theory to generate more informative sentences while maintaining their grammaticality. An experiment led on a corpus of 40 clusters of sentences shows that our system has achieved a very good quality and is better than the state-of-the-art.