In recent years, a number of keyphrase generation (KPG) approaches were proposed consisting of complex model architectures, dedicated training paradigms and decoding strategies. In this work, we opt for simplicity and show how a commonly used seq2seq language model, BART, can be easily adapted to generate keyphrases from the text in a single batch computation using a simple training procedure. Empirical results on five benchmarks show that our approach is as good as the existing state-of-the-art KPG systems, but using a much simpler and easy to deploy framework.
@article{arxiv.2201.05302,
title = {Applying a Generic Sequence-to-Sequence Model for Simple and Effective Keyphrase Generation},
author = {Md Faisal Mahbub Chowdhury and Gaetano Rossiello and Michael Glass and Nandana Mihindukulasooriya and Alfio Gliozzo},
journal= {arXiv preprint arXiv:2201.05302},
year = {2022}
}