Monitoring Fact Preservation, Grammatical Consistency and Ethical Behavior of Abstractive Summarization Neural Models

Iva Marinova, Yolina Petrova, Milena Slavcheva, Petya Osenova, Ivaylo Radev, Kiril Simov


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.
Anthology ID:
2021.ranlp-1.103
Volume:
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
Month:
September
Year:
2021
Address:
Held Online
Editors:
Ruslan Mitkov, Galia Angelova
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
901–909
Language:
URL:
https://aclanthology.org/2021.ranlp-1.103
DOI:
Bibkey:
Cite (ACL):
Iva Marinova, Yolina Petrova, Milena Slavcheva, Petya Osenova, Ivaylo Radev, and Kiril Simov. 2021. Monitoring Fact Preservation, Grammatical Consistency and Ethical Behavior of Abstractive Summarization Neural Models. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 901–909, Held Online. INCOMA Ltd..
Cite (Informal):
Monitoring Fact Preservation, Grammatical Consistency and Ethical Behavior of Abstractive Summarization Neural Models (Marinova et al., RANLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.ranlp-1.103.pdf