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Automatic extractive and generic document summarization based on NMF

Aghdam Mehdi Hosseinzadeh

Journal of Artificial Intelligence and Soft Computing Research

2023

Vol. 13, No. 1

37--49

Nowadays, textual information grows exponentially on the Internet. Text summarization (TS) plays a crucial role in the massive amount of textual content. Manual TS is time-consuming and impractical in some applications with a huge amount of textual information. Automatic text summarization (ATS) is an essential technology to overcome mentioned challenges. Non-negative matrix factorization (NMF) is a useful tool for extracting semantic contents from textual data. Existing NMF approaches only focus on how factorized matrices should be modeled, and neglect the relationships among sentences. These relationships provide better factorization for TS. This paper suggests a novel non-negative matrix factorization for text summarization (NMFTS). The proposed ATS model puts regularizes on pairwise sentences vectors. A new cost function based on the Frobenius norm is designed, and an algorithm is developed to minimize this function by proposing iterative updating rules. The proposed NMFTS extracts semantic content by reducing the size of documents and mapping the same sentences closely together in the latent topic space. Compared with the basic NMF, the convergence time of the proposed method does not grow. The convergence proof of the NMFTS and empirical results on the benchmark data sets show that the suggested updating rules converge fast and achieve superior results compared to other methods.

Classic and convex non-negative matrix visualization in clustering two benchmark data

Bartkowiak A.

Przegląd Elektrotechniczny

2017

R. 93, nr 1

53--59

Both the classic and the convex NMF (Nonnegative Matrix Factorization) yield a parsimonious, lower rank representation of the data. They may yield also an indication on a soft clustering of the data vectors, We analyze two sets of diagnostic data, wine and sonar, for which the classic and convex nonnegative matrix factorization (NMF) behave differently when indicating group membership of the data vectors. The data are given as mxn matrices, with columns denoting objects, and rows - their attributes. We assess the clustering by multivariate graphical visualization methods.

Dla wybranych danych ’wine’ i ’sonar’ znajdujemy – za pomoc¸a NMF (nieujemna faktoryzacja macierzy) – ukrytą strukturę tych macierzy oraz wskazania co do klasteryzacji obiektów przedstawianych w kolumnach danych. Otrzymaną klasteryzację potwierdzamy trzema metodami wielozmiennej wizualizacji wektorów danych.