Study on Extractive Summarization with Global Information

Abstract

Extractive automatic text summarization aims to extract the sentences that can best express the semantics of the full text from the original text to form a summary.It is widely used and studied due to its simplicity and efficiency.Currently,extractive summarization models are mostly based on the local relationship between sentences to obtain importance scores to select sentences.This method ignores the global semantic information of the original text,and the model is more susceptible to the influence of local non-important relationships.Therefore,an extractive summarization model incorporating global semantic information is proposed.After obtaining the representation of sentences and articles,the model learns the relationship between sentences and global information through the sentence-level encoder and global information extraction module and then integrates the extracted global information into the sentence vector.Finally,the sentence score is obtained to determine whether it is a summary sentence.The proposed model can achieve end-to-end training,and two global information extraction techniques based on aspect extraction and neural topic model are studied in the global information extraction module.Experimental results on the public dataset CNN/DailyMail verify the effectiveness of the model integrating global information.

Keywords

• extractive text summarization|global information|aspect extraction|neural topic model