Extractive and Abstractive Event Summarization over Streaming Web Text
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 4002-4002.
Abstract
During crises, information is critical for responders and victims. When the event is significant, as in the case of hurricane Sandy, the amount of content produced by traditional news outlets, relief organizations, and social media vastly overwhelms those trying to monitor the situation. An emerging task in this space is to monitor an event as it unfolds over time by processing an associated stream of documents to produce a rolling update summary containing the most salient information with respect to the event. In this thesis, we develop two extractive summarization systems for streaming text data. Both systems explicitly predict the salience of input stream text to create a rolling summary. Finally, we discuss our proposed work for combining these systems with an abstractive text generation model.
@inproceedings{ijcai2016p575,
title = {Extractive and Abstractive Event Summarization over Streaming Web Text},
author = {Kedzie, Chris and McKeown, Kathleen R.},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {4002--4002},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/575},
}
TY - CPAPER TI - Extractive and Abstractive Event Summarization over Streaming Web Text AU - Kedzie, Chris AU - McKeown, Kathleen R. T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 4002 EP - 4002 UR - https://ijcai.org/proceedings/2016/575 ER -Copied
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