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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 9/2020

09.03.2020 | Original Article

Legal public opinion news abstractive summarization by incorporating topic information

verfasst von: Yuxin Huang, Zhengtao Yu, Junjun Guo, Zhiqiang Yu, Yantuan Xian

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 9/2020

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Abstract

Automatically generate accurate summaries from legal public opinion news can help readers to grasp the main ideas of news quickly. Although many improved sequence-to-sequence models have been proposed for the abstractive text summarization task, these approaches confront two challenges when addressing domain-specific summarization task: (1) the appropriate selection of domain knowledge; (2) the effective manner of integrating domain knowledge into summarization model. In order to tackle the above challenges, this paper selects the pre-training topic information as the legal domain knowledge, which is then integrated into the sequence-to-sequence model to improve the performance of public opinion news summarization. Concretely, two kinds of topic information are utilized: first, the topic words which denote the main aspects of the source document are encoded to guide the decoding process. Furthermore, the predicted output is forced to have a similar topic probability distribution with the source document. We evaluate our model on a large dataset of legal public opinion news collected from micro-blog, and the experimental results show that the proposed model outperforms existing baseline systems under the rouge metrics. To the best of our knowledge, this work represents the first attempt in the legal public opinion domain for text summarization task.
Vorheriger Artikel Unsupervised attribute reduction based on -approximate equal relation in interval-valued information systems
Nächster Artikel An improved artificial bee colony algorithm for balancing local and global search behaviors in continuous optimization

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Metadaten
Titel
Legal public opinion news abstractive summarization by incorporating topic information
verfasst von
Yuxin Huang
Zhengtao Yu
Junjun Guo
Zhiqiang Yu
Yantuan Xian
Publikationsdatum
09.03.2020
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 9/2020
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-020-01093-8

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