Follow the Timeline! Generating Abstractive and Extractive Timeline Summary in Chronological Order

TL;DR

This paper introduces a Unified Timeline Summarizer (UTS) that generates both abstractive and extractive chronological summaries of web documents, utilizing a graph-based event encoder and event-level attention to ensure temporal coherence.

Contribution

The paper presents a novel UTS model with a graph-based encoder and attention mechanism that maintains chronological order in timeline summarization, outperforming previous methods.

Findings

UTS achieves state-of-the-art results on multiple datasets.

The event-level attention improves chronological coherence in summaries.

Extensive experiments validate the effectiveness of the proposed approach.

Abstract

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.