Multiple News Headlines Generation using Page Metadata
Kango Iwama, Yoshinobu Kano · 2019
Multiple headlines of a newspaper article have an important role to express the content of the article accurately and concisely.A headline depends on the content and intent of their article.While a single headline expresses the whole corresponding article, each of multiple headlines expresses different information individually.We suggest an automatic generation method of such diverse multiple headlines in a newspaper.Our generation method is based on the Pointer-Generator Network, using page metadata on a newspaper which can change headline generation behavior.We conducted automatic evaluations for generated headlines.The results show that our method improved ROUGE-1 score by 4.32 points compared to a baseline system.This is the first trial to evaluate such multiple headlines generation as far as we know.These results suggest that our model using page metadata can generate various multiple headlines for an article with better performance.