Automatic paper writing based on a RNN and the TextRank algorithm

Hei Chia Wang, Wei Ching Hsiao, Sheng Han Chang

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Academic research is crucial to the development of science and technology and is an important factor that affects national strength. When writing an academic research paper, a rhetorical structure is typically used to present the paper's ideas, but this task is quite difficult for junior researchers. To solve this problem, some studies have adopted text mining to assist with the writing, but the existing methods still require human intervention to generate sentences. Recently, due to the increasing maturity of deep learning technology and the ability to address the problem of automatic text generation, progress has been made in this area. The highly complex deep learning operations can correctly generate sequences and find correlations between sequences. When a user provides a few keywords and key sentences, the proposed algorithm can generate an introduction section for the user. The results show that the generated introduction is more coherent, clearer, and more fluent than existing summarization methods. In addition, the method proposed in this study improves the accuracy compared with traditional text extraction methods. The manuscript produced by this study has been evaluated to show that the study can produce a comprehensive introduction compared with previous studies.

Original languageEnglish
Article number106767
JournalApplied Soft Computing Journal
Volume97
DOIs
Publication statusPublished - 2020 Dec

All Science Journal Classification (ASJC) codes

  • Software

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