@inproceedings{ge2016news,
author = {Ge, Tao and Cui, Lei and Chang, Baobao and Li, Sujian and Zhou, Ming and Sui, Zhifang},
title = {News Stream Summarization using Burst Information Networks},
booktitle = {EMNLP 2016},
year = {2016},
month = {November},
abstract = {This paper studies summarizing key information from news streams. We propose simple yet effective models to solve the problem based on a novel and promising representation of text streams – Burst Information Networks (BINets). A BINet can be aware of redundant information, allows global analysis of a text stream, and can be efficiently built and dynamically updated, which perfectly fits the demands of text stream summarization. Extensive experiments show that the BINet-based approaches are not only efficient and can be used in a real-time online summarization setting, but also can generate high-quality summaries, outperforming the state-of-the-art approach.},
url = {http://approjects.co.za/?big=en-us/research/publication/news-stream-summarization-using-burst-information-networks/},
}