Text Summarization and Singular Value Decomposition

Bengali Text Summarization Using Singular Value Decomposition

A Multi-Document Multi-Lingual Automatic Summarization System

Abstract. In this paper, a new multidocument multi-lingual text summarization technique, based on singular value decomposition and hierarchical clustering, is proposed. The proposed approach relies on only two resources for any language: a word segmentation system and a dictionary of words along with their document frequencies. The summarizer initially takes a collection of related documents, a...

Clustered Sub-Matrix Singular Value Decomposition

This paper presents an alternative algorithm based on the singular value decomposition (SVD) that creates vector representation for linguistic units with reduced dimensionality. The work was motivated by an application aimed to represent text segments for further processing in a multi-document summarization system. The algorithm tries to compensate for SVD’s bias towards dominant-topic document...

Dimensionality Reduction Aids Term Co-Occurrence Based Multi-Document Summarization

A key task in an extraction system for query-oriented multi-document summarisation, necessary for computing relevance and redundancy, is modelling text semantics. In the Embra system, we use a representation derived from the singular value decomposition of a term co-occurrence matrix. We present methods to show the reliability of performance improvements. We find that Embra performs better with...

Significant Sentence Extraction by Euclidean Distance Based on Singular Value Decomposition

This paper describes an automatic summarization approach that constructs a summary by extracting the significant sentences. The approach takes advantage of the cooccurrence relationships between terms only in the document. The techniques used are principal component analysis (PCA) to extract the significant terms and singular value decompostion (SVD) to find out the significant sentences. The P...