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| RLCPAR: A Rewriting Model for Chinese Patent Abstracts Based on Reinforcement Learning |
Zhang Le1,Leng Jidong1,Lv Xueqiang1,Cui Zhuo2,Wang Lei1,You Xindong1( ) |
1Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science & Technology University, Beijing 100101, China
2School of Information & Communication Engineering, Beijing Information Science & Technology University, Beijing 100101, China |
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Abstract [Objective] This paper proposes a rewriting model for Chinese patent abstracts based on reinforcement learning (RLCPAR), aiming to address the issues of sentence redundancy and low accuracy in rewriting multi-sentence abstracts. [Methods] First, we used the RLCPAR to extract key sentences from patent descriptions with the help of patent term dictionary and reinforcement learning. Then, we generated the candidate abstracts using the Transformer deep neural network. Finally, we merged the candidate abstracts with the original patent abstracts to obtain the rewritten abstracts after semantic de-duplication and sorting. [Results] The proposed model effectively finished the end-to-end rewriting of patent abstracts. The scores of RLCPAR were 56.95%, 37.21% and 51.24% with the ROUGE-1, ROUGE-2 and ROUGE-L criteria. [Limitations] The experimental data, which were mainly on Chinese medicine materials, needs to be expanded to other fields. [Conclusions] The PLCPAR model is much better than other sequence generation methods and improves the rewriting quality of Chinese patent abstracts.
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Received: 27 January 2021
Published: 11 August 2021
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| Fund:National Natural Science Foundation of China(61671070);Open Project Fund of the Tibetan Information Processing and Machine Translation Key Laboratory/the Key Laboratory of Tibetan Information Processing, Ministry of Education(2019Z002) |
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Corresponding Authors:
You Xindong,ORCID： 0000-0002-3351-4599
E-mail: ybyq920@126.com
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