Real-Time Web Scale Event Summarization Using Sequential Decision Making
Computation and Language
2016-05-13 v1
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 time-sensitive F1 metrics.
Cite
@article{arxiv.1605.03664,
title = {Real-Time Web Scale Event Summarization Using Sequential Decision Making},
author = {Chris Kedzie and Fernando Diaz and Kathleen McKeown},
journal= {arXiv preprint arXiv:1605.03664},
year = {2016}
}
Comments
in Proceedings of the 25th International Joint Conference on Artificial Intelligence 2016