Text summarization using a trainable summarizer and latent semantic analysis

By Jen-Yuan Yeh, Hao-Ren Ke, Wei-Pang Yang, I. Meng. Information Processing & Management, 2005

304 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Automatic Text Summarization (2008, methodology): “Genetic Algorithm Model (GA) Genetic Algorithms are a way of solving problems by mimicking the same processes Mother Nature uses [12], [13].”
A new sentence similarity measure and sentence based extractive technique for automatic text summarization (2009, methodology): “In spite of the fact that among our criterion functions the worst result is obtained by criterion function F 2 , but it shows better result than the methods CRF, SVM and Manifold–Ranking.”
Automatic generic document summarization based on non-negative matrix factorization (2009, methodology): “A sentence, therefore, must be represented as a linear combination of many less important semantic features.”
New Alignment Methods for Discriminative Book Summarization (2013, methodology): “Following previous work, we devise sentence-level features that can be readily computed in comparison both with the document in which the sentence in found, and in comparison with the collection of documents as whole (Yeh et al., 2005; Shen et al., 2007).”