DeepTitle -- Leveraging BERT to generate Search Engine Optimized Headlines

• URL: http://arxiv.org/abs/2107.10935v1
• Date: Thu, 22 Jul 2021 21:32:54 GMT
• Title: DeepTitle -- Leveraging BERT to generate Search Engine Optimized Headlines
• Authors: Cristian Anastasiu and Hanna Behnke and Sarah L\"uck and Viktor Malesevic and Aamna Najmi and Javier Poveda-Panter
• Abstract summary: 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 baits for the encoder and decoder. We conduct experiments on a German news data set and achieve a ROUGE-L-gram F-score of 40.02.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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