Neural headline generation: A comprehensive survey

Abstract

Automatic headline generation (HG) is an important natural language processing (NLP) task that aims to obtain a highly compressed text snippet from a document, to exhibit the core concept. Traditional headline generation (HG) techniques predominantly employ text summarization methods to generate short texts, by selecting important information from original documents. In recent years, with the rapid development of deep learning techniques, research on HG has leaned toward neural network-based end-to-end modeling approaches. Pretrained schemes and large language models (LLMs) demonstrate superior capability in generating natural language texts, thereby promoting further exploration on HG studies. However, a quality gap remains between machine-generated and human-written texts, making the generation of attractive and faithful headlines worthy of in-depth research. Therefore, this study presents a review of the most recent technologies on HG, including methods, datasets, and evaluation strategies. Future research directions are outlined, which provide a valuable reference point for HG and other summarization tasks. A collection of reference papers and code sources is available at: https://github.com/xiaona-chang/HGSurvey.