HSDS: An abstractive model for automatic survey generation

Xiao Jian Jiang; Xian Ling Mao; Bo Si Feng; Xiaochi Wei; Bin Bin Bian; Heyan Huang

doi:10.1007/978-3-030-18576-3_5

HSDS: An abstractive model for automatic survey generation

Xiao Jian Jiang, Xian Ling Mao^*, Bo Si Feng, Xiaochi Wei, Bin Bin Bian, Heyan Huang

^*Corresponding author for this work

School of Computer Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Citations (Scopus)

Abstract

Automatic survey generation for a specific research area can quickly give researchers an overview, and help them recognize the technical developing trend of the specific area. As far as we know, the most relevant study with automatic survey generation is the task of automatic related work generation. Almost all existing methods of automatic related work generation extract the important sentences from multiple relevant papers to assemble a related work. However, the extractive methods are far from satisfactory because of poor coherence and readability. In this paper, we propose a novel abstractive method named Hierarchical Seq2seq model based on Dual Supervision (HSDS) to solve problems above. Given multiple scientific papers in the same research area as input, the model aims to generate a corresponding survey. Furthermore, we build a large dataset to train and evaluate the HSDS model. Extensive experiments demonstrate that our proposed model performs better than the state-of-the-art baselines.

Original language	English
Title of host publication	Database Systems for Advanced Applications - 24th International Conference, DASFAA 2019, Proceedings
Editors	Jun Yang, Guoliang Li, Juggapong Natwichai, Joao Gama, Yongxin Tong
Publisher	Springer Verlag
Pages	70-86
Number of pages	17
ISBN (Print)	9783030185756
DOIs	https://doi.org/10.1007/978-3-030-18576-3_5
Publication status	Published - 2019
Event	24th International Conference on Database Systems for Advanced Applications, DASFAA 2019 - Chiang Mai, Thailand Duration: 22 Apr 2019 → 25 Apr 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11446 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	24th International Conference on Database Systems for Advanced Applications, DASFAA 2019
Country/Territory	Thailand
City	Chiang Mai
Period	22/04/19 → 25/04/19

Keywords

Abstractive
Dual Supervision
Survey

Access to Document

10.1007/978-3-030-18576-3_5

Cite this

Jiang, X. J., Mao, X. L., Feng, B. S., Wei, X., Bian, B. B., & Huang, H. (2019). HSDS: An abstractive model for automatic survey generation. In J. Yang, G. Li, J. Natwichai, J. Gama, & Y. Tong (Eds.), Database Systems for Advanced Applications - 24th International Conference, DASFAA 2019, Proceedings (pp. 70-86). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11446 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-18576-3_5

Jiang, Xiao Jian ; Mao, Xian Ling ; Feng, Bo Si et al. / HSDS : An abstractive model for automatic survey generation. Database Systems for Advanced Applications - 24th International Conference, DASFAA 2019, Proceedings. editor / Jun Yang ; Guoliang Li ; Juggapong Natwichai ; Joao Gama ; Yongxin Tong. Springer Verlag, 2019. pp. 70-86 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{5ea9ed30f8a244fab23bfe2221128418,

title = "HSDS: An abstractive model for automatic survey generation",

abstract = "Automatic survey generation for a specific research area can quickly give researchers an overview, and help them recognize the technical developing trend of the specific area. As far as we know, the most relevant study with automatic survey generation is the task of automatic related work generation. Almost all existing methods of automatic related work generation extract the important sentences from multiple relevant papers to assemble a related work. However, the extractive methods are far from satisfactory because of poor coherence and readability. In this paper, we propose a novel abstractive method named Hierarchical Seq2seq model based on Dual Supervision (HSDS) to solve problems above. Given multiple scientific papers in the same research area as input, the model aims to generate a corresponding survey. Furthermore, we build a large dataset to train and evaluate the HSDS model. Extensive experiments demonstrate that our proposed model performs better than the state-of-the-art baselines.",

keywords = "Abstractive, Dual Supervision, Survey",

author = "Jiang, {Xiao Jian} and Mao, {Xian Ling} and Feng, {Bo Si} and Xiaochi Wei and Bian, {Bin Bin} and Heyan Huang",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 24th International Conference on Database Systems for Advanced Applications, DASFAA 2019 ; Conference date: 22-04-2019 Through 25-04-2019",

year = "2019",

doi = "10.1007/978-3-030-18576-3_5",

language = "English",

isbn = "9783030185756",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

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booktitle = "Database Systems for Advanced Applications - 24th International Conference, DASFAA 2019, Proceedings",

address = "Germany",

}

Jiang, XJ, Mao, XL, Feng, BS, Wei, X, Bian, BB & Huang, H 2019, HSDS: An abstractive model for automatic survey generation. in J Yang, G Li, J Natwichai, J Gama & Y Tong (eds), Database Systems for Advanced Applications - 24th International Conference, DASFAA 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11446 LNCS, Springer Verlag, pp. 70-86, 24th International Conference on Database Systems for Advanced Applications, DASFAA 2019, Chiang Mai, Thailand, 22/04/19. https://doi.org/10.1007/978-3-030-18576-3_5

HSDS: An abstractive model for automatic survey generation. / Jiang, Xiao Jian; Mao, Xian Ling; Feng, Bo Si et al.
Database Systems for Advanced Applications - 24th International Conference, DASFAA 2019, Proceedings. ed. / Jun Yang; Guoliang Li; Juggapong Natwichai; Joao Gama; Yongxin Tong. Springer Verlag, 2019. p. 70-86 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11446 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - HSDS

T2 - 24th International Conference on Database Systems for Advanced Applications, DASFAA 2019

AU - Jiang, Xiao Jian

AU - Mao, Xian Ling

AU - Feng, Bo Si

AU - Wei, Xiaochi

AU - Bian, Bin Bin

AU - Huang, Heyan

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2019.

PY - 2019

Y1 - 2019

N2 - Automatic survey generation for a specific research area can quickly give researchers an overview, and help them recognize the technical developing trend of the specific area. As far as we know, the most relevant study with automatic survey generation is the task of automatic related work generation. Almost all existing methods of automatic related work generation extract the important sentences from multiple relevant papers to assemble a related work. However, the extractive methods are far from satisfactory because of poor coherence and readability. In this paper, we propose a novel abstractive method named Hierarchical Seq2seq model based on Dual Supervision (HSDS) to solve problems above. Given multiple scientific papers in the same research area as input, the model aims to generate a corresponding survey. Furthermore, we build a large dataset to train and evaluate the HSDS model. Extensive experiments demonstrate that our proposed model performs better than the state-of-the-art baselines.

AB - Automatic survey generation for a specific research area can quickly give researchers an overview, and help them recognize the technical developing trend of the specific area. As far as we know, the most relevant study with automatic survey generation is the task of automatic related work generation. Almost all existing methods of automatic related work generation extract the important sentences from multiple relevant papers to assemble a related work. However, the extractive methods are far from satisfactory because of poor coherence and readability. In this paper, we propose a novel abstractive method named Hierarchical Seq2seq model based on Dual Supervision (HSDS) to solve problems above. Given multiple scientific papers in the same research area as input, the model aims to generate a corresponding survey. Furthermore, we build a large dataset to train and evaluate the HSDS model. Extensive experiments demonstrate that our proposed model performs better than the state-of-the-art baselines.

KW - Abstractive

KW - Dual Supervision

KW - Survey

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U2 - 10.1007/978-3-030-18576-3_5

DO - 10.1007/978-3-030-18576-3_5

M3 - Conference contribution

AN - SCOPUS:85065537612

SN - 9783030185756

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 70

EP - 86

BT - Database Systems for Advanced Applications - 24th International Conference, DASFAA 2019, Proceedings

A2 - Yang, Jun

A2 - Li, Guoliang

A2 - Natwichai, Juggapong

A2 - Gama, Joao

A2 - Tong, Yongxin

PB - Springer Verlag

Y2 - 22 April 2019 through 25 April 2019

ER -

Jiang XJ, Mao XL, Feng BS, Wei X, Bian BB, Huang H. HSDS: An abstractive model for automatic survey generation. In Yang J, Li G, Natwichai J, Gama J, Tong Y, editors, Database Systems for Advanced Applications - 24th International Conference, DASFAA 2019, Proceedings. Springer Verlag. 2019. p. 70-86. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-18576-3_5

HSDS: An abstractive model for automatic survey generation

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