Recent advances in document summarization

作     者：Yao, Jin-ge Wan, Xiaojun Xiao, Jianguo 

作者机构：Peking Univ Inst Comp Sci & Technol Beijing 100871 Peoples R China Peking Univ MOE Key Lab Computat Linguist Beijing Peoples R China 

出 版 物：《KNOWLEDGE AND INFORMATION SYSTEMS》 (知识和信息系统季刊)

年 卷 期：2017年第53卷第2期

页      面：297-336页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 08[工学] 070105[理学-运筹学与控制论] 081101[工学-控制理论与控制工程] 0701[理学-数学] 071101[理学-系统理论] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Hi-Tech Research and Development Program (863 Program) of China [2015AA015403] National Natural Science Foundation of China IBM Global Faculty Award Program 

主　　题：Document summarization Natural language generation Natural language processing Text mining 

摘      要：The task of automatic document summarization aims at generating short summaries for originally long documents. A good summary should cover the most important information of the original document or a cluster of documents, while being coherent, non-redundant and grammatically readable. Numerous approaches for automatic summarization have been developed to date. In this paper we give a self-contained, broad overview of recent progress made for document summarization within the last 5 years. Specifically, we emphasize on significant contributions made in recent years that represent the state-of-the-art of document summarization, including progress on modern sentence extraction approaches that improve concept coverage, information diversity and content coherence, as well as attempts from summarization frameworks that integrate sentence compression, and more abstractive systems that are able to produce completely new sentences. In addition, we review progress made for document summarization in domains, genres and applications that are different from traditional settings. We also point out some of the latest trends and highlight a few possible future directions.