资源分类
》 资源 》论文》Real-Time Web Scale Event Summarization Using Sequential Decision Making

Real-Time Web Scale Event Summarization Using Sequential Decision Making

2019-11-25 | |  59 |   39 |   0
Abstract We present a system based on sequential decision making for the online summarization of massive document streams, such as those found on the web. Given an event of interest (e.g. “Boston marathon bombing”), our system is able to filter the stream for relevance and produce a series of short text updates describing the event as it unfolds over time. Unlike previous work, our approach is able to jointly model the relevance, comprehensiveness, novelty, and timeliness required by time-sensitive queries. We demonstrate a 28.3% improvement in summary F1 and a 43.8% improvement in timesensitive F1 metrics.

上一篇：Ordering Concepts Based on Common Attribute Intensity

下一篇：Identifying Key Observers to Find Popular Information in Advance

用户评价
全部评价

热门资源

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • The Variational S...

    Unlike traditional images which do not offer in...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Rating-Boosted La...

    The performance of a recommendation system reli...