Transformers for Headline Selection for Russian News Clusters
This work addresses headline selection for Russian news, but it is incremental as it applies existing methods to a specific dataset.
The paper tackled the problem of selecting headlines for Russian news clusters using transformer-based models, achieving 87.28% and 86.60% accuracy on public and private test sets respectively.
In this paper, we explore various multilingual and Russian pre-trained transformer-based models for the Dialogue Evaluation 2021 shared task on headline selection. Our experiments show that the combined approach is superior to individual multilingual and monolingual models. We present an analysis of a number of ways to obtain sentence embeddings and learn a ranking model on top of them. We achieve the result of 87.28% and 86.60% accuracy for the public and private test sets respectively.