基於基因演算法的組合式多文件摘要方法

An Ensemble Approach for Multi-document Summarization using Genetic Algorithms

多文件摘要研究在文件摘要工作中是一個重要的研究項目。透過多文件摘要，將可以減少人們閱讀許多內容相似但相同主題文件的時間。本研究中提出一個基於基因演算法的組合式多文件摘要模型，以四個網路摘要模型，以及四個機率主題網路摘要模型分別建構組合式多文件摘要模型。在這兩個組合式摘要模型中，以基因演算法找出最佳組合權重。在以DUC 2004年至2007年四個資料集的實驗中，這兩個組合摘要模型在ROUGE-1，ROUGE-2，ROUGE-SU4的評分項目上，相較於個別摘要模型都有最好的表現。

Multi-document summarization is an important research task in text summarization. It helps peopleto reduce much time in reading articles of similar contents but with the same topics. In this study, we propose an ensemble model based on genetic algorithms. Two ensemble summarization models are thus constructed, one for four network summarization models, and the other for four probabilistic topic network models. These two ensemble models use genetic algorithms to find the optimal weights. We use the datasets of DUC 2004 to DUC 2007 for performance evaluation. The experimental results show that these two ensemble models can achieve the best performance in ROUGE-1, ROUGE-2, and ROUGE-SU4 than other standalone network models and standalone probabilistic topic network models.