Kazuma Murao


2019

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A Case Study on Neural Headline Generation for Editing Support
Kazuma Murao | Ken Kobayashi | Hayato Kobayashi | Taichi Yatsuka | Takeshi Masuyama | Tatsuru Higurashi | Yoshimune Tabuchi
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)

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.

2018

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Extractive Headline Generation Based on Learning to Rank for Community Question Answering
Tatsuru Higurashi | Hayato Kobayashi | Takeshi Masuyama | Kazuma Murao
Proceedings of the 27th International Conference on Computational Linguistics

User-generated content such as the questions on community question answering (CQA) forums does not always come with appropriate headlines, in contrast to the news articles used in various headline generation tasks. In such cases, we cannot use paired supervised data, e.g., pairs of articles and headlines, to learn a headline generation model. To overcome this problem, we propose an extractive headline generation method based on learning to rank for CQA that extracts the most informative substring from each question as its headline. Experimental results show that our method outperforms several baselines, including a prefix-based method, which is widely used in real services.