Research on extractive text abstract generation method for graph model based on TextRank

Mingsong Dong

doi:10.1117/12.3021351

19 February 2024 Research on extractive text abstract generation method for graph model based on TextRank

Mingsong Dong

Proceedings Volume 13063, Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023); 130631T (2024) https://doi.org/10.1117/12.3021351
Event: Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023), 2023, Changchun, China

Abstract

The traditional TextRank algorithm is based on a graph model, and the extracted abstract generated by it does not deviate from the source text, but there may be issues such as polysemy. The BERT model can capture contextual semantics well. It can directly convert sentences in text into vector form, which can better solve the problem of polysemy. BiLSTM can obtain further information, IDCNN can better handle overfitting problems compared to traditional convolutional neural networks, and CRF can better solve the problem of sentence coherence. The model proposed in this article has been compared with other extraction based text summarization methods on the TTNews dataset, and experiments have shown that the method proposed in this article can obtain meaningful text summaries with significant optimization.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mingsong Dong "Research on extractive text abstract generation method for graph model based on TextRank", Proc. SPIE 13063, Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023), 130631T (19 February 2024); https://doi.org/10.1117/12.3021351

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