Nowadays, neural text generation has made tremendous progress in abstractive
summarization tasks. However, most of the existing summarization models take in
the whole document all at once, which sometimes cannot meet the needs in
practice. Practically, social text streams such as news events and tweets keep
growing from time to time, and can only be fed to the summarization system step
by step. Hence, in this paper, we propose the task of Stepwise Summarization,
which aims to generate a new appended summary each time a new document is
proposed. The appended summary should not only summarize the newly added
content but also be coherent with the previous summary, to form an up-to-date
complete summary. To tackle this challenge, we design an adversarial learning
model, named Stepwise Summary Generator (SSG). First, SSG selectively processes
the new document under the guidance of the previous summary, obtaining polished
document representation. Next, SSG generates the summary considering both the
previous summary and the document. Finally, a convolutional-based discriminator
is employed to determine whether the newly generated summary is coherent with
the previous summary. For the experiment, we extend the traditional two-step
update summarization setting to a multi-step stepwise setting, and re-propose a
large-scale stepwise summarization dataset based on a public story generation
dataset. Extensive experiments on this dataset show that SSG achieves
state-of-the-art performance in terms of both automatic metrics and human
evaluations. Ablation studies demonstrate the effectiveness of each module in
our framework. We also discuss the benefits and limitations of recent large
language models on this task.

本文介绍了一种基于步骤式摘要生成的对抗学习模型，该模型通过选择性地处理新文档并参考先前的摘要，生成与上一次摘要相一致的摘要，实现了最新完整摘要的生成。在大规模的步骤式摘要生成数据集上的实验证明了该模型在自动度量和人工评估方面的优越性。