Abstract
Timeline summarization targets at concisely summarizing the evolution trajectory along the timeline
and existing timeline summarization approaches
are all based on extractive methods. In this paper,
we propose the task of abstractive timeline summarization, which tends to concisely paraphrase the
information in the time-stamped events. 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, we propose a memory-based timeline summarization model (MTS). Concretely, we propose a
time-event memory to establish a timeline, and use
the time position of events on this timeline to guide
generation process. Besides, in each decoding step,
we incorporate event-level information into wordlevel attention to avoid confusion between events.
Extensive experiments are conducted on a largescale real-world dataset, and the results show that
MTS achieves the state-of-the-art performance in
terms of both automatic and human evaluations