面向查询的自动文本摘要技术研究综述

摘要/Abstract

摘要： 对面向查询的自动文本摘要技术进行系统梳理,分析所用方法的基本思想、优缺点,并总结未来的发展方向。通过分析梳理,总结出了四大类面向查询的自动文本摘要技术:基于图模型的方法、基于机器学习的方法、基于聚类的方法和其他方法。在今后的研究过程中,基于神经网络和多模型融合的方法将成为未来研究的热点,在应用层面上,与实际应用场景相结合的算法研究将成为趋势。

关键词: 流排序, 神经网络, 图模型, 文本摘要, 主题模型

Abstract: This paper systematically combed the query-oriented automatic summarization technology,analyzed the basic ideas,advantages and disadvantages of the methods used,and summarized the future development direction.By analyzing,four kinds of query-oriented automatic summarization were summarized:the method based on graph model,the method based on machine learning,the method based on clustering and other methods.In the future,the method based on neural network and multi model fusion will become the focus of future research.In the application level,it will become a trend to study the algorithm combining with the actual application scene.

Key words: Graph model, Manifold ranking, Neural network, Summarization, Topic model

中图分类号:

TP391

王凯祥. 面向查询的自动文本摘要技术研究综述[J]. 计算机科学, 2018, 45(11A): 12-16. https://doi.org/

WANG Kai-xiang. Survey of Query-oriented Automatic Summarization Technology[J]. Computer Science, 2018, 45(11A): 12-16. https://doi.org/

参考文献

[1]CHEN Z M,YI J,ZHAO Y.Automatic summarization based on user’s query expansion[J].Application Research of Computers,2011,28(6):2188-2190.
[2]LUHN H P.The automatic creation of literature abstracts[M].IBM Corp.,1958.
[3]BHASKAR P,BANDYOPADHYAY S.A Query Focused Multi Document Automatic Summarization[C]∥The 24th Pacific Asia Conference on Language,Information and Computation.Tohoku University,Sendai,2010.
[4]康世泽,马宏,黄瑞阳.一种基于神经网络模型的句子排序方法[J].中文信息学报,2016,30(5):195-202.
[5]SUN R,WANG Z,REN Y,et al.Query-Biased Multi-document Abstractive Summarization via Submodular Maximization Using Event Guidance[M].Springer International Publishing,2016.
[6]RUSH A M,CHOPRA S,WESTON J.A Neural Attention Model for Abstractive Sentence Summarization[J].arXiv:1509.00685,2015.
[7]YING W,XIAO X,LI S,et al.Improving Query-Focused Summarization with CNN-Based Similarity[J].Acta Scientiarum Naturalium University Pekinensis,2017,53(2):197-203.
[8]WADHVANI R,KUMAR R,GYANCHANDANI M,et al. RIN-Sum:A System for Query-Specific Multi-Document Extractive Summarization[J].International Journal of Advanced Computer Science & Applications,2017,8(3):106-112.
[9]QUMSIYEH R,NG Y.Web Search Using Summarization on Clustered Web Documents Retrieved by User Queries[C]∥2015IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT).2015:401-404.
[10]SHEN C,LI T.Learning to Rank for Query-Focused Multi-do-cument Summarization[C]∥IEEE International Conference on Data Mining.2012.
[11]何新宇.基于中心词耦合度和PageRank的文本自动摘录算法[J].中山大学研究生学刊(社会科学版),2013(3):43-50.
[12]MIHALCEA R.Graph-based ranking algorithms for sentence extraction,applied to text summarization[C]∥ACL 2004 on Interactive Poster and Demonstration Sessions.2004.
[13]MANI I,BLOEDORN E.Multi-document summarization by graph search and matching[C]∥Fourteenth National Conference on Artificial Intelligence and Ninth Conference on Innovative Applications of Artificial Intelligence.1997.
[14]PANDIT S R,POTEY M A.A Query Specific Graph Based Approach to Multi-document Text Summarization:Simultaneous Cluster and Sentence Ranking[C]∥International Conference on Machine Intelligence and Research Advancement.2014.
[15]LEI K,ZENG Y F.A Novel Biased Diversity Ranking Model for Query-Oriented Multi-Document Summarization[J].Applied Mechanics & Materials,2013,380-384:2811-2816.
[16]CAI X,LI W.Mutually Reinforced Manifold-Ranking Based Relevance Propagation Model for Query-Focused Multi-Document Summarization[J].IEEE Transactions on Audio Speech &Language Processing,2012,20(5):1597-1607.
[17]BADRINATH R,VENKATASUBRAMANIYAN S,MADHAVAN C E V.Improving Query Focused Summarization Using Look-Ahead Strategy[C]∥European Conference on Advances in Information Retrieval.Springer-Verlag,2011:641-652.
[18]WEI F,LI W,HE Y.Document-Aware Graph Models for Query-oriented Multi-document Summarization[M].Springer Berlin Heidelberg,2011.
[19]CANHASI E,KONONENKO I.Weighted archetypal analysis of the multi-element graph for query-focused multi-document summarization[J].Expert Systems with Applications,2014,41(2):535-543.
[20]CANHASI E.Query Focused Multi-document Summarization Based on Five-Layered Graph and Universal Paraphrastic Embeddings[C]∥Computer Science On-line Conference.Springer,Cham,2017:220-228.
[21]SAKAMOTO K,SHIBUKI H,MORI T,et al.Fusion of heterogeneous information in graph-based ranking for query-biased summarization[C]∥BSG@SIGIR.20015:19-22.
[22]WANG W,WEI F,LI W,et al.HyperSum:hypergraph based semi-supervised sentence ranking for query-oriented summarization[C]∥ACM Conference on Information and Knowledge Management.ACM,2009:1855-1858.
[23]D SILVA S,JOSHI N,RAO S,et al.Improved Algorithms for Document Classification & Query-based Multi-Document Summarization[J].International Journal of Engineering and Technology,2012,3(4):404-409.
[24]XIONG S,JI D.Query-focused multi-document summarization using hypergraph-based ranking[J].Information Processing & Management,2016,52(4):670-681.
[25]ZHENG H T,GUO J M,JIANG Y,et al.Query-Focused Multi-document Summarization Based on Concept Importance[C]∥Pacific-Asia Conference on Knowledge Discovery and Data Mi-ning.Springer International Publishing,2016:443-453.
[26]LI C G.Query-Focused Multi-Document Summarization Based on Dominant Sets Cluster[J].Computer Systems & Applications,2014,3(1):50-55.
[27]SURESHRAO D Y,SUBHASH S B,DASHORE P.Analysis of Query Dependent Summarization Using Clustering Techniques[J].International Journal of Computer Technology & Electro-nics Engineering,2012,2(1):213-217.
[28]PATIL G V,MRUNAL B.Query Dependant Single Document Summarization using Partitional Clustering:K-Means Clustering Approach[J].International Journal of Computer Science Engineering & Technolo,2011,1(5):191.
[29]徐晓丹.基于子主题和用户查询的多文档摘要系统[J].计算机系统应用,2011,20(3):112-115.
[30]YANG L,CAI X.Semi-Supervised Co-Clustering for Query-Orien-ted Theme-based Summarization[J].Research Journal of Applied Sciences Engineering & Technology,2012,4(18):3410-3414.
[31]NAVEEN G K R,NEDUNGADI P.Query-based Multi-Document Summarization by Clustering of Documents[C]∥International Conference on Interdisciplinary Advances in Applied Computing.2014.
[32]LUO W,ZHUANG F,HE Q,et al.Exploiting relevance,cove-rage,and novelty for query-focused multi-document summarization[J].Knowledge-Based Systems,2013,46(1):33-42.
[33]LI Y,LI S.Query-focused multi-document summarization:Combining a topic model with graph-based semi-supervised learning[C]∥The 25th International Conference on Computational Liguistics:Technical Papers.2014:1197-1207.
[34]YANG G.A Novel Contextual Topic Model for Query-Focused Multi-document Summarization[C]∥IEEE International Conference on TOOLS with Artificial Intelligence.2014.
[35]SONDUR P P B S.Multi-Document Text Summarization using Mutual Reinforcement and Relevance Propagation Models Added with Query and Features Profile[J].International Journal of Advanced Computer Research,2013,3(3):59.
[36]SHAFIEIBAVANI E,EBRAHIMI M,WONG R,et al.A Query-Based Summarization Service from Multiple News Sources[C]∥IEEE International Conference on Services Computing.2016.
[37]LI J,LI S.Query-focused Multi-document Summarization:Combining a Novel Topic Model with Graph-based Semi-supervised Learning[J].arXiv:1212.2036,2012.
[38]KATIYAR S,BORGOHAIN S.Lexical Similarity Based Query-Focused Summarization Using Artificial Immune Systems[M].Springer International Publishing,2015.
[39]LI J,LI S.A Novel Feature-based Bayesian Model for Query Focused Multi-document Summarization[J].Transactions of the Association for Computational Linguistics,2014,1:89-98.
[40]OUYANG Y,LI W,LI S,et al.Applying regression models to query-focused multi-document summarization[J].Information Processing & Management An International Journal,2011,47(2):227-237.
[41]AZAR M Y,SIRTS K,ALIOD D M,et al.Query-Based Single Document Summarization Using an Ensemble Noisy Auto-Encoder[C]∥Australasian Language Technology Association Workshop.2015.
[42]FEIGENBLAT G,ROITMAN H,BONI O,et al.Unsupervised Query-Focused Multi-Document Summarization using the Cross Entropy Method[C]∥The International ACM SIGIR Con-ference.2017.
[43]VALIZADEH M,BRAZDIL P.Exploring actor-object relationships for query-focused multi-document summarization[J].Soft Computing,2014,19(11):1-13.
[44]LIU Y,ZHONG S H,LI W.Query-Oriented Multi-Document Summarization via Unsupervised Deep Learning[J].Expert Systems with Applications,2012,2(21):1699-1705.
[45]NEMA P,KHAPRA M,LAHA A,et al.Diversity driven Attention Model for Query-based Abstractive Summarization[J].arXiv:1704.08300,2017.
[46]森田一.Enhanced Models for Query-Oriented Extractive Summarization[J].Journal of the Japanese Society for Artificial Intelligence,2015,30(1):125.
[47]SONG W,YU Q,XU Z,et al.Multi-aspect query summarization by composite query[C]∥International ACM SIGIR Conference on Research and Development in Information Retrieval.ACM,2012:325-334.
[48]WANG L, RAGHAVAN H, CARDIE C,et al.Query-Focused Opinion Summarization for User-Generated Content[J].arXiv:1606.05702,2016.
[49]M R V V,Y P K S,SATYANANDA REDDY C.A Hybrid Method for Query based Automatic Summarization System[J].International Journal of Computer Applications,2014,68(6):39-43.
[50]SOWMYA K S.Query-oriented Unsupervised Multi-document Summarization on Big Data[C]∥International Conference on Computing Communication and Networking Technologies.2016.
[51]SHAFIEIBAVANI E,EBRAHIMI M,WONG R,et al.A Query-Based Summarization Service from Multiple News Sources[C]∥IEEE International Conference on Services Computing.2016.

相关文章 15

[1]	宁晗阳, 马苗, 杨波, 刘士昌. 密码学智能化研究进展与分析 Research Progress and Analysis on Intelligent Cryptology 计算机科学, 2022, 49(9): 288-296. https://doi.org/10.11896/jsjkx.220300053
[2]	周芳泉, 成卫青. 基于全局增强图神经网络的序列推荐 Sequence Recommendation Based on Global Enhanced Graph Neural Network 计算机科学, 2022, 49(9): 55-63. https://doi.org/10.11896/jsjkx.210700085
[3]	周乐员, 张剑华, 袁甜甜, 陈胜勇. 多层注意力机制融合的序列到序列中国连续手语识别和翻译 Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion 计算机科学, 2022, 49(9): 155-161. https://doi.org/10.11896/jsjkx.210800026
[4]	李宗民, 张玉鹏, 刘玉杰, 李华. 基于可变形图卷积的点云表征学习 Deformable Graph Convolutional Networks Based Point Cloud Representation Learning 计算机科学, 2022, 49(8): 273-278. https://doi.org/10.11896/jsjkx.210900023
[5]	郝志荣, 陈龙, 黄嘉成. 面向文本分类的类别区分式通用对抗攻击方法 Class Discriminative Universal Adversarial Attack for Text Classification 计算机科学, 2022, 49(8): 323-329. https://doi.org/10.11896/jsjkx.220200077
[6]	王润安, 邹兆年. 基于物理操作级模型的查询执行时间预测方法 Query Performance Prediction Based on Physical Operation-level Models 计算机科学, 2022, 49(8): 49-55. https://doi.org/10.11896/jsjkx.210700074
[7]	陈泳全, 姜瑛. 基于卷积神经网络的APP用户行为分析方法 Analysis Method of APP User Behavior Based on Convolutional Neural Network 计算机科学, 2022, 49(8): 78-85. https://doi.org/10.11896/jsjkx.210700121
[8]	朱承璋, 黄嘉儿, 肖亚龙, 王晗, 邹北骥. 基于注意力机制的医学影像深度哈希检索算法 Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism 计算机科学, 2022, 49(8): 113-119. https://doi.org/10.11896/jsjkx.210700153
[9]	檀莹莹, 王俊丽, 张超波. 基于图卷积神经网络的文本分类方法研究综述 Review of Text Classification Methods Based on Graph Convolutional Network 计算机科学, 2022, 49(8): 205-216. https://doi.org/10.11896/jsjkx.210800064
[10]	闫佳丹, 贾彩燕. 基于双图神经网络信息融合的文本分类方法 Text Classification Method Based on Information Fusion of Dual-graph Neural Network 计算机科学, 2022, 49(8): 230-236. https://doi.org/10.11896/jsjkx.210600042
[11]	金方焱, 王秀利. 融合RACNN和BiLSTM的金融领域事件隐式因果关系抽取 Implicit Causality Extraction of Financial Events Integrating RACNN and BiLSTM 计算机科学, 2022, 49(7): 179-186. https://doi.org/10.11896/jsjkx.210500190
[12]	彭双, 伍江江, 陈浩, 杜春, 李军. 基于注意力神经网络的对地观测卫星星上自主任务规划方法 Satellite Onboard Observation Task Planning Based on Attention Neural Network 计算机科学, 2022, 49(7): 242-247. https://doi.org/10.11896/jsjkx.210500093
[13]	费星瑞, 谢逸. 基于HMM-NN的用户点击流识别 Click Streams Recognition for Web Users Based on HMM-NN 计算机科学, 2022, 49(7): 340-349. https://doi.org/10.11896/jsjkx.210600127
[14]	赵冬梅, 吴亚星, 张红斌. 基于IPSO-BiLSTM的网络安全态势预测 Network Security Situation Prediction Based on IPSO-BiLSTM 计算机科学, 2022, 49(7): 357-362. https://doi.org/10.11896/jsjkx.210900103
[15]	齐秀秀, 王佳昊, 李文雄, 周帆. 基于概率元学习的矩阵补全预测融合算法 Fusion Algorithm for Matrix Completion Prediction Based on Probabilistic Meta-learning 计算机科学, 2022, 49(7): 18-24. https://doi.org/10.11896/jsjkx.210600126

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed