An algorithm for natural language generation via text extracting

AI Li-si; TANG Wei-hong; FU Yun-bin; DONG Qi-min; ZHENG Jian-bing; GAO Ming

doi:10.3969/j.issn.1000-5641.2018.04.007

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Jul. 2018

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Article Navigation > Journal of East China Normal University (Natural Sciences) > 2018 > (4): 70-79

AI Li-si, TANG Wei-hong, FU Yun-bin, DONG Qi-min, ZHENG Jian-bing, GAO Ming. An algorithm for natural language generation via text extracting[J]. Journal of East China Normal University (Natural Sciences), 2018, (4): 70-79. doi: 10.3969/j.issn.1000-5641.2018.04.007

Citation:

AI Li-si, TANG Wei-hong, FU Yun-bin, DONG Qi-min, ZHENG Jian-bing, GAO Ming. An algorithm for natural language generation via text extracting[J]. Journal of East China Normal University (Natural Sciences), 2018, (4): 70-79. doi: 10.3969/j.issn.1000-5641.2018.04.007

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An algorithm for natural language generation via text extracting

doi: 10.3969/j.issn.1000-5641.2018.04.007

1.
School of Data Science and Engineering, East China Normal University, Shanghai 200062, China
2.
Shanghai Agricultural Technology Extension and Service Center, Shanghai 201103, China
3.
Vocational and Technical Education Center of Linxi County, Linxi Inner Mongolia 025250, China

Received Date: 2017-06-19
Publish Date: 2018-07-25

Abstract

Abstract

The aim of natural language generation is to achieve a state where machines can generate text automatically. This would reduce the workload of human language workers and helps us deliver real-time, concise news coverage to readers. It could be applied to many applications, such as question and answers systems, automatic news writing, incident reporting, and so on. The challenge has been one of the open problems for both academia and industry. In this paper, we model the issue as a keyword covering problem and propose an unsupervised approach to extract text for natural language generation.The experimental results illustrate that the algorithm is effective for large-scale corpus; the text coverage is more comprehensive and the generated text is closer to the manual text produced by an individual.
- natural language generation,
- keyword cover problem,
- informative,
- redundancy

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References(18)

References

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