Open AccessAutomatic Text Summarization Using Deep Reinforcement Learning and Beyond
Author(s) -
Gang Sun,
Zhongxin Wang,
Jia Zhao
Publication year - 2021
Publication title -
informacinės technologijos ir valdymas
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.286
H-Index - 19
eISSN - 2335-884X
pISSN - 1392-124X
DOI - 10.5755/j01.itc.50.3.28047
Subject(s) - automatic summarization , computer science , artificial intelligence , natural language processing , metric (unit) , information overload , deep learning , baseline (sea) , information retrieval , multi document summarization , key (lock) , world wide web , operations management , oceanography , economics , computer security , geology
In the era of big data, information overload problems are becoming increasingly prominent. It is challengingfor machines to understand, compress and filter massive text information through the use of artificial intelligencetechnology. The emergence of automatic text summarization mainly aims at solving the problem ofinformation overload, and it can be divided into two types: extractive and abstractive. The former finds somekey sentences or phrases from the original text and combines them into a summarization; the latter needs acomputer to understand the content of the original text and then uses the readable language for the human tosummarize the key information of the original text. This paper presents a two-stage optimization method forautomatic text summarization that combines abstractive summarization and extractive summarization. First,a sequence-to-sequence model with the attention mechanism is trained as a baseline model to generate initialsummarization. Second, it is updated and optimized directly on the ROUGE metric by using deep reinforcementlearning (DRL). Experimental results show that compared with the baseline model, Rouge-1, Rouge-2,and Rouge-L have been increased on the LCSTS dataset and CNN/DailyMail dataset.