Applying Transformer-based Text Summarization for Keyphrase Generation

TL;DR

This paper explores transformer-based abstractive summarization models for keyphrase generation, comparing their effectiveness to traditional methods across multiple datasets and analyzing the impact of phrase ordering strategies.

Contribution

It demonstrates the potential of transformer models for keyphrase generation and highlights the importance of phrase ordering strategies in improving performance.

Findings

Transformer models are effective in generating keyphrases according to F1-score and BERTScore.

Summarization models tend to produce words not in the original keyphrase list, reducing ROUGE-1 effectiveness.

Ordering strategies significantly influence keyphrase generation performance.

Abstract

Keyphrases are crucial for searching and systematizing scholarly documents. Most current methods for keyphrase extraction are aimed at the extraction of the most significant words in the text. But in practice, the list of keyphrases often includes words that do not appear in the text explicitly. In this case, the list of keyphrases represents an abstractive summary of the source text. In this paper, we experiment with popular transformer-based models for abstractive text summarization using four benchmark datasets for keyphrase extraction. We compare the results obtained with the results of common unsupervised and supervised methods for keyphrase extraction. Our evaluation shows that summarization models are quite effective in generating keyphrases in the terms of the full-match F1-score and BERTScore. However, they produce a lot of words that are absent in the author's list of keyphrases, which makes summarization models ineffective in terms of ROUGE-1. We also investigate several ordering strategies to concatenate target keyphrases. The results showed that the choice of strategy affects the performance of keyphrase generation.