Exploring Fine-tuned Generative Models for Keyphrase Selection: A Case Study for Russian

TL;DR

This study investigates the application of fine-tuned generative transformer models for keyphrase selection in Russian scientific texts, demonstrating notable in-domain improvements and potential for cross-domain adaptation.

Contribution

It introduces the use of multiple generative models like ruT5, ruGPT, mT5, and mBART for keyphrase selection in Russian, highlighting their performance gains over traditional extraction methods.

Findings

mBART achieved up to 4.9% in BERTScore improvement

In-domain performance improved significantly with generative models

Cross-domain results showed potential despite lower performance

Abstract

Keyphrase selection plays a pivotal role within the domain of scholarly texts, facilitating efficient information retrieval, summarization, and indexing. In this work, we explored how to apply fine-tuned generative transformer-based models to the specific task of keyphrase selection within Russian scientific texts. We experimented with four distinct generative models, such as ruT5, ruGPT, mT5, and mBART, and evaluated their performance in both in-domain and cross-domain settings. The experiments were conducted on the texts of Russian scientific abstracts from four domains: mathematics & computer science, history, medicine, and linguistics. The use of generative models, namely mBART, led to gains in in-domain performance (up to 4.9% in BERTScore, 9.0% in ROUGE-1, and 12.2% in F1-score) over three keyphrase extraction baselines for the Russian language. Although the results for cross-domain usage were significantly lower, they still demonstrated the capability to surpass baseline performances in several cases, underscoring the promising potential for further exploration and refinement in this research field.