Abstractive summarization models typically learn to capture the salient
information from scratch implicitly. Recent literature adds extractive
summaries as guidance for abstractive summarization models to provide hints of
salient content and achieves better performance. However, extractive summaries
as guidance could be over strict, leading to information loss or noisy signals.
Furthermore, it cannot easily adapt to documents with various abstractiveness.
As the number and allocation of salience content pieces vary, it is hard to
find a fixed threshold deciding which content should be included in the
guidance. In this paper, we propose a novel summarization approach with a
flexible and reliable salience guidance, namely SEASON (SaliencE Allocation as
Guidance for Abstractive SummarizatiON). SEASON utilizes the allocation of
salience expectation to guide abstractive summarization and adapts well to
articles in different abstractiveness. Automatic and human evaluations on two
benchmark datasets show that the proposed method is effective and reliable.
Empirical results on more than one million news articles demonstrate a natural
fifteen-fifty salience split for news article sentences, providing a useful
insight for composing news articles.

本文提出了一种新的自由灵活的关键信息指引式文本摘要方法 SEASON，通过利用关键信息的分配来指导抽象文本摘要并适用于不同抽象程度的文章，从而证明 SEASON 方法在两个基准数据集上表现优越，并在一百万篇新闻文章上证明了一个自然的十五五十关键信息分配。