@inproceedings{marinova-etal-2021-monitoring,
title = "Monitoring Fact Preservation, Grammatical Consistency and Ethical Behavior of Abstractive Summarization Neural Models",
author = "Marinova, Iva and
Petrova, Yolina and
Slavcheva, Milena and
Osenova, Petya and
Radev, Ivaylo and
Simov, Kiril",
editor = "Mitkov, Ruslan and
Angelova, Galia",
booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)",
month = sep,
year = "2021",
address = "Held Online",
publisher = "INCOMA Ltd.",
url = "https://aclanthology.org/2021.ranlp-1.103",
pages = "901--909",
abstract = "The paper describes a system for automatic summarization in English language of online news data that come from different non-English languages. The system is designed to be used in production environment for media monitoring. Automatic summarization can be very helpful in this domain when applied as a helper tool for journalists so that they can review just the important information from the news channels. However, like every software solution, the automatic summarization needs performance monitoring and assured safe environment for the clients. In media monitoring environment the most problematic features to be addressed are: the copyright issues, the factual consistency, the style of the text and the ethical norms in journalism. Thus, the main contribution of our present work is that the above mentioned characteristics are successfully monitored in neural automatic summarization models and improved with the help of validation, fact-preserving and fact-checking procedures.",
}
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<abstract>The paper describes a system for automatic summarization in English language of online news data that come from different non-English languages. The system is designed to be used in production environment for media monitoring. Automatic summarization can be very helpful in this domain when applied as a helper tool for journalists so that they can review just the important information from the news channels. However, like every software solution, the automatic summarization needs performance monitoring and assured safe environment for the clients. In media monitoring environment the most problematic features to be addressed are: the copyright issues, the factual consistency, the style of the text and the ethical norms in journalism. Thus, the main contribution of our present work is that the above mentioned characteristics are successfully monitored in neural automatic summarization models and improved with the help of validation, fact-preserving and fact-checking procedures.</abstract>
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%0 Conference Proceedings
%T Monitoring Fact Preservation, Grammatical Consistency and Ethical Behavior of Abstractive Summarization Neural Models
%A Marinova, Iva
%A Petrova, Yolina
%A Slavcheva, Milena
%A Osenova, Petya
%A Radev, Ivaylo
%A Simov, Kiril
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F marinova-etal-2021-monitoring
%X The paper describes a system for automatic summarization in English language of online news data that come from different non-English languages. The system is designed to be used in production environment for media monitoring. Automatic summarization can be very helpful in this domain when applied as a helper tool for journalists so that they can review just the important information from the news channels. However, like every software solution, the automatic summarization needs performance monitoring and assured safe environment for the clients. In media monitoring environment the most problematic features to be addressed are: the copyright issues, the factual consistency, the style of the text and the ethical norms in journalism. Thus, the main contribution of our present work is that the above mentioned characteristics are successfully monitored in neural automatic summarization models and improved with the help of validation, fact-preserving and fact-checking procedures.
%U https://aclanthology.org/2021.ranlp-1.103
%P 901-909
Markdown (Informal)
[Monitoring Fact Preservation, Grammatical Consistency and Ethical Behavior of Abstractive Summarization Neural Models](https://aclanthology.org/2021.ranlp-1.103) (Marinova et al., RANLP 2021)
ACL