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| Generating Patent Text Abstracts Based on Improved Multi-head Attention Mechanism |
Shi Guoliang1( ),Zhou Shu1,2,Wang Yunfeng2,Shi Chunjiang2,Liu Liang2 |
1Business School, Hohai University, Nanjing 211100, China
2Bank of Jiangsu, Nanjing 210006, China |
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Abstract [Objective] This paper addresses the problem of single-bias in patent text summarization caused by the single input structure of the patent text in patent texts. It also addresses the issues of repeated generation, the need for conciseness and fluency, and the loss of original information in generating abstracts. [Methods] We designed a patent text abstract generation model based on an improved multi-head attention mechanism (IMHAM). Firstly, we designed two cosine similarity-based algorithms based on the logical structure of the patent text to address the single structure issue and select the most important patent document. Then, we established a sequence-to-sequence model with a multi-head attention mechanism to learn the feature representation of patent text. Meanwhile, we added self-attention layers at the encoder and decoder levels. Next, we modified the attention function to address the problem of repetitive generation. Finally, we added an improved pointer network structure to solve the problem of original information loss. [Results] On the publicly available patent text dataset, the Rouge-1, Rouge-2, and Rouge-L scores of the proposed model were 3.3%, 2.4%, and 5.5% higher than the MedWriter baseline model. [Limitations] The proposed model is more applicable for documents with multiple structures and cannot fully utilize the algorithm for selecting the most important ones from single-structured documents. [Conclusions] The proposed model has good generalization ability in improving the quality of summary generation for text with multi-document structures.
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Received: 25 May 2022
Published: 09 August 2023
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| Fund:Fundamental Research Funds for the Central Universities(B200207036) |
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Corresponding Authors:
Shi Guoliang，ORCID：0000-0001-8672-9342，E-mail：shigl@hhu.edu.cn。
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