文本摘要研究进展与趋势

明拓思宇; 陈鸿昶

doi:10.11959/j.issn.2096-109x.2018048

您当前的位置：

首页 >

文章列表页 >

文本摘要研究进展与趋势

综述 | 更新时间：2024-06-03

- 文本摘要研究进展与趋势
- Research progress and trend of text summarization
- 网络与信息安全学报 2018年4卷第6期页码：1-10
- 作者机构：
- 作者简介：
  
  [ "明拓思宇（1994-），男，湖南长沙人，国家数字交换系统工程技术研究中心硕士生，主要研究方向为文本摘要。" ]
  [ "陈鸿昶（1964-），男，河南郑州人，国家数字交换系统工程技术研究中心教授、博士生导师，主要研究方向为电信网信息安全。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61601513)
- DOI：10.11959/j.issn.2096-109x.2018048
  中图分类号： TP393
- 网络首发：2018-06，
  
  纸质出版：2018-06-15
- 稿件说明：
移动端阅览
明拓思宇, 陈鸿昶. 文本摘要研究进展与趋势[J]. 网络与信息安全学报, 2018,4(6):1-10.

Tuosiyu MING, Hongchang CHEN. Research progress and trend of text summarization[J]. Chinese Journal of Network and Information Security, 2018, 4(6): 1-10.
明拓思宇, 陈鸿昶. 文本摘要研究进展与趋势[J]. 网络与信息安全学报, 2018,4(6):1-10. DOI： 10.11959/j.issn.2096-109x.2018048.

Tuosiyu MING, Hongchang CHEN. Research progress and trend of text summarization[J]. Chinese Journal of Network and Information Security, 2018, 4(6): 1-10. DOI： 10.11959/j.issn.2096-109x.2018048.

摘要

随着互联网上的信息呈爆炸式增长，如何从海量信息中提取有用信息成了一个关键的技术问题。文本摘要技术能够从大数据中压缩提炼出精炼简洁的文档信息，有效降低用户的信息过载问题，成为研究热点。分类整理分析了近些年来国内外的文本摘要方法及其具体实现，将传统方法和深度学习摘要方法的优缺点进行了对比分析，并对今后的研究方向进行了合理展望。

Abstract

With the explosive growth of information on the Internet

how to extract useful information from massive information has become a key technical issue.The text summarization technology can compress and extract refined and concise document information from big data

effectively reducing the user information overload problem

and it has become a research hotspot.The domestic and foreign text summarization methods and their concrete realization in recent years were analyzed

the advantages and disadvantages between traditional methods and deep learning summary methods were compared

and a reasonable outlook for future research directions was made.

关键词

Keywords

references

CHENG J , LAPATA M . Neural summarization by extracting sentences and words [J ] . ar Xiv preprint ar Xiv:1603.07252 , 2016 .

NEMA P , KHAPRA M , LAHA A , et al . Diversity driven attention model for query-based abstractive summarization [J ] . ar Xiv preprint ar Xiv:1704.08300 , 2017 .

LI P , LAM W , BING L , et al . Deep recurrent generative decoder for abstractive text summarization [J ] . ar Xiv preprint ar Xiv:1708.00625 , 2017 .

BING L , LI P , LIAO Y , et al . Abstractive multi-document summarization via phrase selection and merging [J ] . ar Xiv preprint ar Xiv:1506.01597 , 2015 .

LI C , QIAN X , LIU Y . Using supervised bigram-based ilp for extractive summarization [C ] // The 51st Annual Meeting of the Association for Computational Linguistics . 2013 : 1004 - 1013 .

VEENA G , GUPTA D , JAGANADH J , et al . A graph based conceptual mining model for abstractive text summarization [J ] . Indian Journal of Science and Technology , 2016 , 9 ( S1 ).

DANESH S , SUMNER T , MARTIN J H . Sgrank:combining statistical and graphical methods to improve the state of the art in unsupervised keyphrase extraction [C ] // The Fourth Joint Conference on Lexical and Computational Semantics . 2015 : 117 - 126 .

FLORESCU C , CARAGEA C . Position Rank:an unsupervised approach to keyphrase extraction from scholarly documents [C ] // The 55th Annual Meeting of the Association for Computational Linguistics . 2017 : 1105 - 1115 .

LUHN H P . The automatic creation of literature abstracts [J ] . IBM Journal of Research and Development , 1958 , 2 ( 2 ): 159 - 165 .

BAXENDALE P B . Machine-made index for technical literature—an experiment [J ] . IBM Journal of Research and Development , 1958 , 2 ( 4 ): 354 - 361 .

EDMUNDSON H P . New methods in automatic extracting [J ] . Journal of the ACM , 1969 , 16 ( 2 ): 264 - 285 .

SALTON G , YU C T . On the construction of effective vocabularies for information retrieval [C ] // ACM SIGIR Forum . 1973 : 48 - 60 .

施聪莺 , 徐朝军 , 杨晓江 . TFIDF 算法研究综述 [J ] . 计算机应用 , 2009 , 29 ( B06 ): 167 - 170 .

SHI C Y , XU C J , YANG X J . Study of TFIDF algorithm [J ] . Journal of Computer Applications , 2009 , 29 ( B06 ): 167 - 170 .

徐文海 , 温有奎 . 一种基于 TFIDF 方法的中文关键词抽取算法 [J ] . 情报理论与实践 , 2008 , 31 ( 2 ): 298 - 302 .

XU W H , WEN Y K . A Chinese keyword extraction algorithm based on TFIDF method [J ] . Information Studies:Theory ＆ Application , 2008 , 31 ( 2 ): 298 - 302 .

SUQIN Z B S H M . An improved text feature weighting algorithm based on TFIDF [J ] . Computer Applications and Software , 2011 , 2 : 7 .

李静月 , 李培峰 , 朱巧明 . 一种改进的 TFIDF 网页关键词提取方法 [J ] . 计算机应用与软件 , 2011 , 28 ( 5 ): 25 - 27 .

LI J Y , LI P F , ZHU Q M . An improved TFIDF-based approach to extract key words from Wed pages [J ] . Computer Applications and Software , 2011 , 28 ( 5 ): 25 - 27 .

EL-BELTAGY S R , RAFEA A . Kp-miner:participation in semeval-2 [C ] // The 5th International Workshop on Semantic Evaluation , 2010 : 190 - 193 .

PADMALAHARI E , KUMAR D V N S , PRASAD S . Automatic text summarization with statistical and linguistic features using successive thresholds [C ] // 2014 International Conference on Advanced Communication Control and Computing Technologies (ICACCCT) . 2014 : 1519 - 1524 .

MILLER G A . Word Net:a lexical database for English [J ] . Communications of the ACM , 1995 , 38 ( 11 ): 39 - 41 .

BARZILAY R , ELHADAD M . Using lexical chains for text summarization [J ] . Advances in Automatic Text Summarization , 1999 : 111 - 121 .

JAIN A , GAUR A . Summarizing long historical documents using significance and utility calculation using Word Net [J ] . Imperial Journal of Interdisciplinary Research , 2017 , 3 ( 3 ).

SILBER H G , MCCOY K F . Efficient text summarization using lexical chains [C ] // The 5th International Conference on Intelligent user interfaces . ACM , 2000 : 252 - 255 .

KOLLA M . Automatic text summarization using lexical chains:algorithms and experiments [D ] . University of Lethbridge , 2004 .

POURVALI M , ABADEH M S . Automated text summarization base on lexicales chain and graph using of wordnet and wikipedia knowledge base [J ] . ar Xiv preprint ar Xiv:1203.3586 , 2012 .

HOU S , HUANG Y , FEI C , et al . Holographic lexical chain and its application in chinese text summarization [C ] // Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint Conference on Web and Big Data . 2017 : 266 - 281 .

LYNN H M , CHOI C , KIM P . An improved method of automatic text summarization for Web contents using lexical chain with semantic-related terms [J ] . Soft Computing , 2018 , 22 ( 12 ): 4013 - 4023 .

PAL A R , SAHA D . An approach to automatic text summarization using Word Net [C ] // IEEE International Conference on Advance Computing Conference (IACC) . 2014 : 1169 - 1173 .

PAGE L , BRIN S , MOTWANI R , et al . The Page Rank citation ranking:bringing order to the Web [R ] . Stanford Info Lab , 1999 .

KLEINBERG J M , KUMAR R , RAGHAVAN P , et al . The Web as a graph:measurements,models,and methods [C ] // International Computing and Combinatorics Conference , 1999 : 1 - 17 .

MIHALCEA R , . Graph-based ranking algorithms for sentence extraction,applied to text summarization [C ] // Proceedings of the ACL 2004 on Interactive Poster And Demonstration Sessions . Association for Computational Linguistics , 2004 :20.

WAN X , XIAO J . Single document keyphrase extraction using neighborhood knowledge [C ] // AAAI . 2008 ,8: 855 - 860 .

GOLLAPALLI S D , CARAGEA C . Extracting keyphrases from research papers using citation networks [C ] // AAAI . 2014 : 1629 - 1635 .

KHAN A , SALIM N , FARMAN H , et al . Abstractive text summarization based on improved semantic graph approach [J ] . International Journal of Parallel Programming , 2018 : 1 - 25 .

AL-KHASSAWNEH Y A , SALIM N , JARRAH M . Improving triangle-graph based text summarization using hybrid similarity function [J ] . Indian Journal of Science and Technology , 2017 , 10 ( 8 ).

WEI F , LI W , LU Q , et al . A document-sensitive graph model for multi-document summarization [J ] . Knowledge and Information Systems , 2010 , 22 ( 2 ): 245 - 259 .

GE S S , ZHANG Z , HE H . Weighted graph model based sentence clustering and ranking for document summarization [C ] // 2011 4th International Conference on Interaction Sciences (ICIS) . 2011 : 90 - 95 .

NGUYEN-HOANG T A , NGUYEN K , TRAN Q V . TSGVi:a graph-based summarization system for Vietnamese documents [J ] . Journal of Ambient Intelligence and Humanized Computing , 2012 , 3 ( 4 ): 305 - 313 .

耿焕同 , 蔡庆生 , 赵鹏 , 等 . 一种基于词共现图的文档自动摘要研究 [J ] . 情报学报 , 2005 , 24 ( 6 ): 652 - 1 .

GENG H T , CAI Q S , ZHAO P , et al . Research on document automatic summarization based on word co-occurrence [J ] . Journal of the China Society for Scientific and Technical Information , 2005 , 24 ( 6 ): 652 - 1 .

SEHGAL S , KUMAR B , RAMPAL L , et al . A modification to graph based approach for extraction based automatic text summarization [M ] // Progress in Advanced Computing and Intelligent Engineering . Singapore Springer Press , 2018 : 373 - 378 .

YOUSEFI-AZAR M , HAMEY L . Text summarization using unsupervised deep learning [J ] . Expert Systems with Applications , 2017 , 68 : 93 - 105 .

ARRAS L , HORN F , MONTAVON G , et al . What is relevant in a text document? an interpretable machine learning approach [J ] . PloSone , 2017 , 12 ( 8 ):e0181142

THU H N T . An optimization text summarization method based on naive bayes and topic word for single syllable language [J ] . Applied Mathematical Sciences , 2014 , 8 ( 3 ): 99 - 115 .

SILVA G , FERREIRA R , LINS R D , et al . Automatic text document summarization based on machine learning [C ] // 2015 ACM Symposium on Document Engineering . ACM , 2015 : 191 - 194 .

NISHIKAWA H , ARITA K , TANAKA K , et al . Learning to generate coherent summary with discriminative hidden semi-markov model [C ] // The 25th International Conference on Computational Linguistics:Technical Papers . 2014 : 1648 - 1659 .

ALLAHYARI M , POURIYEH S , ASSEFI M , et al . A brief survey of text mining:classification,clustering and extraction techniques [J ] . ar Xiv preprint ar Xiv:1707.02919 , 2017 .

KUPIEC J , PEDERSEN J , CHEN F . A trainable document summarizer [C ] // The 18th annual International ACM SIGIR Conference on Research and Development in Information Retrieval . 1995 : 68 - 73 .

CONROY J M,O'LEARY D P , . Text summarization via hidden markov models [C ] // The 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval . 2001 : 406 - 407 .

LIN C Y , . Training a selection function for extraction [C ] // The Eighth International Conference on Information and Knowledge Management . ACM , 1999 : 55 - 62 .

HINTON G E , OSINDERO S , TEH Y W . A fast learning algorithm for deep belief nets [J ] . Neural Computation , 2006 , 18 ( 7 ): 1527 - 1554 .

MRKŠIĆ N , VULIĆ I , SÉAGHDHA D Ó , et al . Semantic specialisation of distributional word vector spaces using monolingual and cross-lingual constraints [J ] . ar Xiv preprint ar Xiv:1706.00374 , 2017 .

XIONG Z , SHEN Q , WANG Y , et al . Paragraph vector representation based on word to vector and CNN learning [J ] . CMC:Computers,Materials ＆ Continua , 2018 , 55 ( 2 ): 213 - 227 .

WANG X , ZHANG H , LIU Y . Sentence vector model based on implicit word vector expression [J ] . IEEE Access , 2018 , 6 : 17455 - 17463 .

SUTSKEVER I , VINYALS O , Le Q V . Sequence to sequence learning with neural networks [C ] // Advances in neural information processing systems . 2014 : 3104 - 3112 .

NALLAPATI R , XIANG B , ZHOU B . Sequence-to-sequence rnns for text summarization [J ] . ar Xiv preprint ar Xiv:1602.06023v1 , 2016 .

RUSH A M , CHOPRA S , WESTON J . A neural attention model for abstractive sentence summarization [J ] . ar Xiv preprint ar Xiv:1509.00685 , 2015 .

CHOPRA S , AULI M , RUSH A M . Abstractive sentence summarization with attentive recurrent neural networks [C ] // The 2016 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies . 2016 : 93 - 98 .

NALLAPATI R , ZHOU B , GULCEHRE C , et al . Abstractive text summarization using sequence-to-sequence RNNS and beyond [J ] . ar Xiv preprint ar Xiv:1602.06023v5 , 2016 .

CAO Z , LI W , LI S , et al . Attsum:Joint learning of focusing and summarization with neural attention [J ] . ar Xiv preprint ar Xiv:1604.00125 , 2016 .

SEE A , LIU P J , Manning C D . Get to the point:summarization with pointer-generator networks [J ] . ar Xiv preprint ar Xiv:1704.04368 , 2017 .

ABADI M , BARHAM P , CHEN J , et al . Tensor Flow:a system for large-scale machine learning [C ] // OSDI . 2016 : 265 - 283 .

SUTSKEVER I , VINYALS O , LE Q V . Sequence to sequence learning with neural networks [C ] // Advances in Neural Information Processing Systems . 2014 : 3104 - 3112 .

GEHRING J , AULI M , GRANGIER D , et al . Convolutional sequence to sequence learning [J ] . ar Xiv preprint ar Xiv:1705.03122 , 2017 .

LIU L , LU Y , YANG M , et al . Generative adversarial network for abstractive text summarization [J ] . ar Xiv preprint ar Xiv:1711.09357 , 2017 .

GOODFELLOW I , POUGET-ABADIE J , MIRZA M , et al . Generative adversarial nets [C ] // Advances in Neural Information Processing Systems . 2014 : 2672 - 2680 .

TAN J , WAN X , XIAO J . Abstractive document summarization with a graph-based attentional neural model [C ] // The 55th Annual Meeting of the Association for Computational Linguistics . 2017 : 1171 - 1181 .

MIHALCEA R , TARAU P . Textrank:bringing order into text [C ] // Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing . 2004 .

ERKAN G , RADEV D R . Lexrank:graph-based lexical centrality as salience in text summarization [J ] . Journal of Artificial Intelligence Research , 2004 , 22 : 457 - 479 .

浏览量

10367

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

DNS异常检测：大规模实验分析

恶意软件检测和分类可视化技术综述

基于双路特征提取和自适应融合的钓鱼网站检测

基于代码相似性的软件漏洞检测研究进展

基于扩散模型的机器学习后门攻击防御方法