EST: Extractive Summary Generator Using Inter-Sentence Information

Abstract

The task of summary generation is important in a society with rapidly growing data. We can expect that summary generation tasks, which extract only the necessary information from a large amount of information, will attract more and more attention in the future. This study focuses on Summary generation for the purpose of assisting people to acquire information. We consider an extractive summary generation model using unsupervised learning. Specifically, we propose EST (Extractive Summarization from masked sentences prediction Transformer), which generates extractive summaries from inter-sentence information.In our evaluation, although the average of the evaluation results was lower than that of the related studies, it was found that some articles obtained higher scores than others.