Abstractive text summarization model combining a hierarchical attention mechanism and multiobjective reinforcement learning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10254656" target="_blank" >RIV/61989100:27240/24:10254656 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0957417424002215?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0957417424002215?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.eswa.2024.123356" target="_blank" >10.1016/j.eswa.2024.123356</a>

Result language

angličtina

Original language name

Abstractive text summarization model combining a hierarchical attention mechanism and multiobjective reinforcement learning

Original language description

Text summarization research is significant and challenging in the domain of natural language processing. Abstractive text summarization mainly uses the encoder-decoder framework, wherein the encoder component does not have a sufficient semantic comprehension of the input text, and there are exposure biases and semantic inconsistencies between the reference and generated summaries during the training process. We propose an improved encoder-decoder model that incorporates a hierarchical attention mechanism and multiobjective reinforcement learning. The encoder introduces a multihead self-attention mechanism to allow for the acquisition of more comprehensive semantic information from multiple angles and dimensions, while the decoder introduces a pointer-generator network to solve the out-of-vocabulary problem. Multiobjective reinforcement learning methods are constructed throughout the training process to optimize the model in terms of addressing exposure bias, maintaining semantic consistency, and enhancing readability. The results of the comparative experiments demonstrate that the proposed model significantly improved in terms of the ROUGE evaluation metric, and the generated summaries were semantically similar to the reference summaries. (C) 2024 Elsevier Ltd

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Expert Systems with Applications

ISSN

0957-4174

e-ISSN

1873-6793

Volume of the periodical

248

Issue of the periodical within the volume

AUG 15 2024

Country of publishing house

GB - UNITED KINGDOM

Number of pages

11

Pages from-to

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UT code for WoS article

001186959800001

EID of the result in the Scopus database

2-s2.0-85185263358