# Keyphrase Generation: A Text Summarization Struggle

## TL;DR

This paper investigates whether text summarization models can generate keyphrases for scientific articles and finds that current neural summarization methods do not outperform simpler existing approaches.

## Contribution

The study introduces a large dataset of scientific metadata and systematically evaluates neural summarization models for keyphrase generation, revealing their limitations.

## Key findings

- Neural summarization models do not outperform simpler methods.
- Large datasets and computational resources do not guarantee better keyphrases.
- Current models struggle to generate valuable keyphrases not present in the text.

## Abstract

Authors' keyphrases assigned to scientific articles are essential for recognizing content and topic aspects. Most of the proposed supervised and unsupervised methods for keyphrase generation are unable to produce terms that are valuable but do not appear in the text. In this paper, we explore the possibility of considering the keyphrase string as an abstractive summary of the title and the abstract. First, we collect, process and release a large dataset of scientific paper metadata that contains 2.2 million records. Then we experiment with popular text summarization neural architectures. Despite using advanced deep learning models, large quantities of data and many days of computation, our systematic evaluation on four test datasets reveals that the explored text summarization methods could not produce better keyphrases than the simpler unsupervised methods, or the existing supervised ones.