Yolina Petrova


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
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

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.