id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-219817-dqmztvo4	Oghaz, Toktam A.	Probabilistic Model of Narratives Over Topical Trends in Social Media: A Discrete Time Model	2020-04-14		.txt	text/plain	5198	289	45	Our proposed framework is designed as a probabilistic topic model, with categorical time distribution, followed by extractive text summarization. The shortage of labeled data for text analysis has encouraged researchers to develop novel unsupervised algorithms that consider co-occurrence of words in documents as well as emerging new techniques such as exploiting an additional source of information similar to Wikipedia knowledge-based topic models [37, 38] . We believe that what differentiates a narrative model 2 from topic analysis and summarization approaches is the ability to extract relevant sequences of text relative to the corresponding series of events associated with the same topic over time. Finally, we demonstrate that our proposed model discovers time localized topics over events that approximates the distribution of user activities on social media platforms. Our focus in the present work is on probabilistic topic modeling and extractive text summarization to provide descriptive narratives for the underlying events that occurred over a period of time.	./cache/cord-219817-dqmztvo4.txt	./txt/cord-219817-dqmztvo4.txt