Exploiting Timelines to Enhance Multi-document Summarization
Jun-Ping Ng, Yan Chen, Min‐Yen Kan, Zhoujun Li · 2014
We study the use of temporal information in the form of timelines to enhance multidocument summarization.We employ a fully automated temporal processing system to generate a timeline for each input document.We derive three features from these timelines, and show that their use in supervised summarization lead to a significant 4.1% improvement in ROUGE performance over a state-of-the-art baseline.In addition, we propose TIMEMMR, a modification to Maximal Marginal Relevance that promotes temporal diversity by way of computing time span similarity, and show its utility in summarizing certain document sets.We also propose a filtering metric to discard noisy timelines generated by our automatic processes, to purify the timeline input for summarization.By selectively using timelines guided by filtering, overall summarization performance is increased by a significant 5.9%.