  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.
