Many social media news writers are not professionally trained. Therefore,
social media platforms have to hire professional editors to adjust amateur
headlines to attract more readers. We propose to automate this headline editing
process through neural network models to provide more immediate writing support
for these social media news writers. To train such a neural headline editing
model, we collected a dataset which contains articles with original headlines
and professionally edited headlines. However, it is expensive to collect a
large number of professionally edited headlines. To solve this low-resource
problem, we design an encoder-decoder model which leverages large scale
pre-trained language models. We further improve the pre-trained model's quality
by introducing a headline generation task as an intermediate task before the
headline editing task. Also, we propose Self Importance-Aware (SIA) loss to
address the different levels of editing in the dataset by down-weighting the
importance of easily classified tokens and sentences. With the help of
Pre-training, Adaptation, and SIA, the model learns to generate headlines in
the professional editor's style. Experimental results show that our method
significantly improves the quality of headline editing comparing against
previous methods.

通过神经网络模型自动化社交媒体新闻标题的编辑过程，通过使用先前预训练的语言模型及提出的 SIA loss（自我重要性感知）损失函数解决低资源情况下数据集困难的问题，有效提高了新闻编辑质量。