論 文Papers

CONFERENCE (INTERNATIONAL)

A Case Study on Neural Headline Generation for Editing Support

Kazuma Murao, Ken Kobayashi, Hayato Kobayashi, Taichi Yatsuka1, Takeshi Masuyama, Tatsuru Higurashi, Yoshimune Tabuchi

2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2019), 2019/6

Category:

自然言語処理 (Natural Language Processing) 機械学習 (Machine Learning)

Abstract:
There have been many studies on neural headline generation models trained with a lot of (article, headline) pairs. However, there are few situations for putting such models into practical use in the real world since news articles typically already have corresponding headlines. In this paper, we describe a practical use case of neural headline generation in a news aggregator, where dozens of professional editors constantly select important news articles and manually create their headlines, which are much shorter than the original headlines. Specifically, we show how to deploy our model to an editing support tool and report the results of comparing the behavior of the editors before and after the release.
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A Case Study on Neural Headline Generation for Editing Support(外部サイト/External Site Link)