In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) has been redirecting to new domain since Jan. 1st, 2025.
See More
Algorithm Research & Explore
|
App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 8
2399-2403

Data-to-text generation methods based on hierarchical structural representation

Gong Yonggang
Guo Yixing
Lian Xiaoqin
Ma Guochun
Wang Xi
Liu Hongyu
School of Artificial Intelligence, Beijing Technology & Business University, Beijing 100048, China
DOI: 10.19734/j.issn.1001-3695.2022.11.0768

Abstract

Recent data-to-text generation methods have widely adopted encoder-decoder architectures or their variants, but these methods fail to identify the different importance of information in different parts of the data, resulting in poor performance in selecting appropriate content and ranking. To address these problems, this paper proposed a data-to-text generation method based on hierarchical structural representation, which consisted of a planning phase and a generation phase. The planning phase enhanced the representation of the semantic space through multi-level attention of entity-level and record-level, and the output plan represented a high-level representation of the important content, while the plan was input to the generator in the generation phase to obtain the final text. Extensive experiments on two datasets generated by data-to-text show that the method generates texts have more accurate descriptions of data and higher quality compared to existing data-to-text generation methods. The proposed method provides some guidance for the research of data-to-text generation.

Foundation Support

“十三五”时期北京市属高校高水平教师队伍建设支持计划资助项目(CIT&TCD201904037)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.11.0768
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 8
Section: Algorithm Research & Explore
Pages: 2399-2403
Serial Number: 1001-3695(2023)08-024-2399-05

Publish History

[2023-02-14] Accepted Paper
[2023-08-05] Printed Article

Cite This Article

龚永罡, 郭怡星, 廉小亲, 等. 基于层次化结构表示的数据到文本生成方法 [J]. 计算机应用研究, 2023, 40 (8): 2399-2403. (Gong Yonggang, Guo Yixing, Lian Xiaoqin, et al. Data-to-text generation methods based on hierarchical structural representation [J]. Application Research of Computers, 2023, 40 (8): 2399-2403. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)