  Nowadays, time-stamped web documents related to a general news query floods
spread throughout the Internet, and timeline summarization targets concisely
summarizing the evolution trajectory of events along the timeline. Unlike
traditional document summarization, timeline summarization needs to model the
time series information of the input events and summarize important events in
chronological order. To tackle this challenge, in this paper, we propose a
Unified Timeline Summarizer (UTS) that can generate abstractive and extractive
timeline summaries in time order. Concretely, in the encoder part, we propose a
graph-based event encoder that relates multiple events according to their
content dependency and learns a global representation of each event. In the
decoder part, to ensure the chronological order of the abstractive summary, we
propose to extract the feature of event-level attention in its generation
process with sequential information remained and use it to simulate the
evolutionary attention of the ground truth summary. The event-level attention
can also be used to assist in extracting summary, where the extracted summary
also comes in time sequence. We augment the previous Chinese large-scale
timeline summarization dataset and collect a new English timeline dataset.
Extensive experiments conducted on these datasets and on the out-of-domain
Timeline 17 dataset show that UTS achieves state-of-the-art performance in
terms of both automatic and human evaluations.
