id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_27d5tr326feydgcx32t4nhl424	Jiwei Li	A Novel Feature-based Bayesian Model for Query Focused Multi-document Summarization	2013	10	.pdf	application/pdf	6037	780	68	A Novel Feature-based Bayesian Model for Query Focused Multi-document Supervised learning methods and LDA based topic propose a novel supervised approach that can incorporate rich sentence features into Bayesian topic both topic model and feature based supervised learning methods. topic models have widely been applied in multidocument summarization in that Bayesian approaches can offer clear and rigorous probabilistic interpretations for summaries(Daume and Marcu, use of both useful text features and the latent semantic structures from by LDA topic model. of combining topic model with feature based supervised learning. feature based Bayesian model S-sLDA for multidocument summarization. Haghighi and Vanderwende (2009) proposed topicsum and hiersum which use a LDA-like topic model step in combining topic model with supervised feature based regression for sentence scoring in summarization. The problem of Celikyilmaz and HakkaniTurs model is that topic model and feature based regression are two separate processes and the score of	./cache/work_27d5tr326feydgcx32t4nhl424.pdf	./txt/work_27d5tr326feydgcx32t4nhl424.txt