@inproceedings{nakshatri-etal-2023-using,
title = "Using {LLM} for Improving Key Event Discovery: Temporal-Guided News Stream Clustering with Event Summaries",
author = "Nakshatri, Nishanth and
Liu, Siyi and
Chen, Sihao and
Roth, Dan and
Goldwasser, Dan and
Hopkins, Daniel",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.274",
doi = "10.18653/v1/2023.findings-emnlp.274",
pages = "4162--4173",
abstract = "Understanding and characterizing the discus- sions around key events in news streams is important for analyzing political discourse. In this work, we study the problem of identification of such key events and the news articles associated with those events from news streams. We propose a generic framework for news stream clustering that analyzes the temporal trend of news articles to automatically extract the underlying key news events that draw significant media attention. We characterize such key events by generating event summaries, based on which we form document clusters in an unsupervised fashion. We evaluate our simple yet effective framework, and show that it produces more coherent event-focused clusters. To demonstrate the utility of our approach, and facilitate future research along the line, we use our framework to construct KeyEvents, a dataset of 40k articles with 611 key events from 11 topics.",
}
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<abstract>Understanding and characterizing the discus- sions around key events in news streams is important for analyzing political discourse. In this work, we study the problem of identification of such key events and the news articles associated with those events from news streams. We propose a generic framework for news stream clustering that analyzes the temporal trend of news articles to automatically extract the underlying key news events that draw significant media attention. We characterize such key events by generating event summaries, based on which we form document clusters in an unsupervised fashion. We evaluate our simple yet effective framework, and show that it produces more coherent event-focused clusters. To demonstrate the utility of our approach, and facilitate future research along the line, we use our framework to construct KeyEvents, a dataset of 40k articles with 611 key events from 11 topics.</abstract>
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%0 Conference Proceedings
%T Using LLM for Improving Key Event Discovery: Temporal-Guided News Stream Clustering with Event Summaries
%A Nakshatri, Nishanth
%A Liu, Siyi
%A Chen, Sihao
%A Roth, Dan
%A Goldwasser, Dan
%A Hopkins, Daniel
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F nakshatri-etal-2023-using
%X Understanding and characterizing the discus- sions around key events in news streams is important for analyzing political discourse. In this work, we study the problem of identification of such key events and the news articles associated with those events from news streams. We propose a generic framework for news stream clustering that analyzes the temporal trend of news articles to automatically extract the underlying key news events that draw significant media attention. We characterize such key events by generating event summaries, based on which we form document clusters in an unsupervised fashion. We evaluate our simple yet effective framework, and show that it produces more coherent event-focused clusters. To demonstrate the utility of our approach, and facilitate future research along the line, we use our framework to construct KeyEvents, a dataset of 40k articles with 611 key events from 11 topics.
%R 10.18653/v1/2023.findings-emnlp.274
%U https://aclanthology.org/2023.findings-emnlp.274
%U https://doi.org/10.18653/v1/2023.findings-emnlp.274
%P 4162-4173
Markdown (Informal)
[Using LLM for Improving Key Event Discovery: Temporal-Guided News Stream Clustering with Event Summaries](https://aclanthology.org/2023.findings-emnlp.274) (Nakshatri et al., Findings 2023)
ACL