DeepTitle -- Leveraging BERT to generate Search Engine Optimized Headlines
Abstract
Automated headline generation for online news articles is not a trivial task - machine generated titles need to be grammatically correct, informative, capture attention and generate search traffic without being "click baits" or "fake news". In this paper we showcase how a pre-trained language model can be leveraged to create an abstractive news headline generator for German language. We incorporate state of the art fine-tuning techniques for abstractive text summarization, i.e. we use different optimizers for the encoder and decoder where the former is pre-trained and the latter is trained from scratch. We modify the headline generation to incorporate frequently sought keywords relevant for search engine optimization. We conduct experiments on a German news data set and achieve a ROUGE-L-gram F-score of 40.02. Furthermore, we address the limitations of ROUGE for measuring the quality of text summarization by introducing a sentence similarity metric and human evaluation.
Cite
@article{arxiv.2107.10935,
title = {DeepTitle -- Leveraging BERT to generate Search Engine Optimized Headlines},
author = {Cristian Anastasiu and Hanna Behnke and Sarah Lück and Viktor Malesevic and Aamna Najmi and Javier Poveda-Panter},
journal= {arXiv preprint arXiv:2107.10935},
year = {2021}
}
Comments
9 pages, 4 figures