Background Summarization of Event Timelines
Adithya Pratapa Kevin Small Markus Dreyer
Generating concise summaries of news events is a challenging natural language
processing task. While journalists often curate timelines to highlight key
sub-events, newcomers to a news event face challenges in catching up on its
historical context. In this paper, we address this need by introducing the task
of background news summarization, which complements each timeline update with a
background summary of relevant preceding events. We construct a dataset by
merging existing timeline datasets and asking human annotators to write a
background summary for each timestep of each news event. We establish strong
baseline performance using state-of-the-art summarization systems and propose a
query-focused variant to generate background summaries. To evaluate background
summary quality, we present a question-answering-based evaluation metric,
Background Utility Score (BUS), which measures the percentage of questions
about a current event timestep that a background summary answers. Our
experiments show the effectiveness of instruction fine-tuned systems such as
Flan-T5, in addition to strong zero-shot performance using GPT-3.5.