id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-027316-echxuw74	Modarresi, Kourosh	Detecting the Most Insightful Parts of Documents Using a Regularized Attention-Based Model	2020-05-22		.txt	text/plain	2116	148	49	This work uses a regularized attention-based method to detect the most influential part(s) of any given document or text. The model uses an encoder-decoder architecture based on attention-based decoder with regularization applied to the corresponding weights. Deep Learning has become a main model in natural language processing applications [6, 7, 11, 22, 38, 55, 64, 71, 75, 78-81, 85, 88, 94] . Though, modified version of RNN like LSTM and GRU have been improvement over RNN (recurrent neural networks) in dealing with vanishing gradients and long-term memory loss, still they suffer from many deficiencies. Given the complexity of these dependencies, a neural network model is used to compute these weights. The embedding regularization is, α Embedding Error 2 (6) Input to any model has to be a number and hence the raw input of words or text sequence needs to be transformed to continuous numbers. Learning phrase representations using RNN encoder-decoder for statistical machine translation	./cache/cord-027316-echxuw74.txt	./txt/cord-027316-echxuw74.txt