<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In the context of the rapid advancement of big data and artificial intelligence, there has been an unprecedented surge in text-based information. This proliferation necessitates the development of efficient and accurate techniques for text summarization. This paper addresses this need by articulating the challenges associated with text summarization and key information extraction. We introduce a novel model that integrates multi-task learning with an attention mechanism to enhance the summarization and extraction of long texts. Furthermore, we establish a loss function for the model, calibrated against the discrepancy observed during the training phase. Empirical evaluations were conducted through simulated experiments after pre-processing the data via the proposed extraction model. These evaluations indicate that the model achieves optimal performance in the iterative training range of 55 to 65. When benchmarked against comparative models, our model demonstrates superior performance in extracting long text summaries and key information, evidenced by the metrics on the Daily Mail dataset (mean scores: 40.19, 16.42, 35.48) and the Gigaword dataset (mean scores: 34.38, 16.21, 31.38). Overall, the model developed in this study proves to be highly effective and practical in extracting long text summaries and key information, thereby significantly enhancing the efficiency of processing textual data.</p>
</abstract>

In the context of the rapid advancement of big data and artificial intelligence, there has been an unprecedented surge in text-based information. This proliferation necessitates the development of efficient and accurate techniques for text summarization. This paper addresses this need by articulating the challenges associated with text summarization and key information extraction. We introduce a novel model that integrates multi-task learning with an attention mechanism to enhance the summarization and extraction of long texts. Furthermore, we establish a loss function for the model, calibrated against the discrepancy observed during the training phase. Empirical evaluations were conducted through simulated experiments after pre-processing the data via the proposed extraction model. These evaluations indicate that the model achieves optimal performance in the iterative training range of 55 to 65. When benchmarked against comparative models, our model demonstrates superior performance in extracting long text summaries and key information, evidenced by the metrics on the Daily Mail dataset (mean scores: 40.19, 16.42, 35.48) and the Gigaword dataset (mean scores: 34.38, 16.21, 31.38). Overall, the model developed in this study proves to be highly effective and practical in extracting long text summaries and key information, thereby significantly enhancing the efficiency of processing textual data.

Long Text Summarization and Key Information Extraction in a Multi-Task Learning Framework

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In the context of the rapid advancement of big data and artificial intelligence, there has been an unprecedented surge in text-based information. This...

Long Text Summarization and Key Information Extraction in a Multi-Task Learning Framework

Published Online: Jul 05, 2024

Page range: -

Received: Mar 01, 2024

Accepted: May 18, 2024

DOI: https://doi.org/10.2478/amns-2024-1659

Keywords
Multi-task learning, Attention mechanism, Loss function, Long text summarization, Key information extraction

© 2024 Ming Lu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Long Text Summarization and Key Information Extraction in a Multi-Task Learning Framework

Published Online: Jul 05, 2024

Page range: -

Received: Mar 01, 2024

Accepted: May 18, 2024

DOI: https://doi.org/10.2478/amns-2024-1659

KeywordsMulti-task learning, Attention mechanism, Loss function, Long text summarization, Key information extraction

© 2024 Ming Lu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
Multi-task learning, Attention mechanism, Loss function, Long text summarization, Key information extraction