An algorithm for natural language generation via text extracting

doi:10.3969/j.issn.1000-5641.2018.04.007

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.

Key words: natural language generation, keyword cover problem, informative, redundancy

CLC Number:

TP391

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 Sc, 2018, 2018(4): 70-79.

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