Multi-Document Extractive Text Summarization via Deep Learning Approach
Afsaneh Rezaei, Sina Dami, Parisa Daneshjoo · 2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI) · 2019
Today, given the huge amount of information, summarization has become one of the most applicable topics in data mining that can help users gain access to useful data over a short period of time. In this study, two multi-document extractive text Summarization systems are introduced. The major objective of this research is to use autoencoder neural network and deep belief network separately for scoring sentences in a document to compare their performances. Deep neural networks can improve the results by generating new features. The abovementioned systems were tested on DUC 2007 dataset and evaluated using ROUGE-1 and ROUGE-2 criteria. The results show a better performance of autoencoder network versus deep belief network. It is also possible to compare these values with results of other systems to realize the effectiveness of the proposed methods.